Input: The goal of this research is to develop a spoken language system that will demonstrate the usefulness of voice input for interactive problem solving. The system will accept continuous speech, and will handle multiple speakers without explicit speaker enrollment. Combining speech recognition and natural language processing to achieve speech understanding, the system will be demonstrated in an application domain relevant to the DoD. The objective of this project is to develop a robust and high-performance speech recognition system using a segment-based approach to phonetic recognition. The recognition system will eventually be integrated with natural language processing to achieve spoken language understanding.

spoken language system

robust and high-performance speech recognition system

segment-based approach

recognition system

voice input

continuous speech

interactive problem solving

speech recognition

natural language processing

speech understanding

phonetic recognition

natural language processing

spoken language understanding

system

spoken language system

interactive problem solving

voice input

interactive problem solving

speech recognition

speech understanding

natural language processing

speech understanding

segment-based approach

robust and high-performance speech recognition system

segment-based approach

phonetic recognition

phonetic recognition

robust and high-performance speech recognition system

recognition system

spoken language understanding

natural language processing

spoken language understanding

speech recognition

natural language processing

natural language processing

recognition system


Input: Spelling-checkers have become an integral part of most text processing software. From different reasons among which the speed of processing prevails they are usually based on dictionaries of word forms instead of words. This approach is sufficient for languages with little inflection such as English, but fails for highly inflective languages such as Czech, Russian, Slovak or other Slavonic languages. We have developed a special method for describing inflection for the purpose of building spelling-checkers for such languages. The speed of the resulting program lies somewhere in the middle of the scale of existing spelling-checkers for English and the main dictionary fits into the standard 360K floppy, whereas the number of recognized word forms exceeds 6 million -LRB- for Czech -RRB-. Further, a special method has been developed for easy word classification.

Spelling-checkers

text processing software

spelling-checkers

word classification

they

approach

method

languages

program

method

dictionaries of word forms

inflection

inflection

languages

English

highly inflective languages

Czech

Russian

Slovak

Slavonic languages

English

Czech

spelling-checkers

Spelling-checkers

text processing software

dictionaries of word forms

they

languages

approach

method

inflection

method

spelling-checkers

spelling-checkers

languages

English

spelling-checkers

method

word classification

inflection

languages

English

languages

Czech

highly inflective languages

Russian

highly inflective languages

Slovak

highly inflective languages

Slavonic languages

highly inflective languages

Czech

Russian

Russian

Slovak

Slovak

Slavonic languages


Input: We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons. Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. Finally, we show how this new tagger achieves state-of-the-art results in a supervised, non-training intensive framework.

HMM tagger

unsupervised methods

HMM training

it

algorithms

tagger

unsupervised and supervised case

part-of-speech tagging

supervised, non-training intensive framework

accuracy

lexical probabilities

unsupervised and supervised case

it

accuracy

algorithms

supervised, non-training intensive framework

tagger

unsupervised methods

part-of-speech tagging


Input: We propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient -LRB- SVRG -RRB- and Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-. For a large family of penalized empirical risk minimization problems, our methods exploit data dependent local smoothness of the loss functions near the optimum, while maintaining convergence guarantees. Our bounds are the first to quantify the advantage gained from local smoothness which are significant for some problems significantly better. Empirically, we provide thorough numerical results to back up our theory. Additionally we present algorithms exploiting local smoothness in more aggressive ways, which perform even better in practice.

non-uniform sampling strategies

stochastic optimization algorithms

Stochastic Variance Reduced Gradient -LRB- SVRG -RRB-

Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-

linear convergence

data dependent local smoothness

loss functions

optimum

convergence guarantees

local smoothness

local smoothness

penalized empirical risk minimization problems

methods

theory

algorithms

non-uniform sampling strategies

stochastic optimization algorithms

methods

penalized empirical risk minimization problems

data dependent local smoothness

methods

local smoothness

algorithms

linear convergence

stochastic optimization algorithms

data dependent local smoothness

loss functions

Stochastic Variance Reduced Gradient -LRB- SVRG -RRB-

stochastic optimization algorithms

Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-

stochastic optimization algorithms

Stochastic Variance Reduced Gradient -LRB- SVRG -RRB-

Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-


Input: Statistical language modeling remains a challenging task, in particular for morphologically rich languages. Recently, new approaches based on factored language models have been developed to address this problem. These models provide principled ways of including additional conditioning variables other than the preceding words, such as morphological or syntactic features. However, the number of possible choices for model parameters creates a large space of models that can not be searched exhaustively. This paper presents an entirely data-driven model selection procedure based on genetic search, which is shown to outperform both knowledge-based and random selection procedures on two different language modeling tasks -LRB- Arabic and Turkish -RRB-.

Statistical language modeling

factored language models

entirely data-driven model selection procedure

genetic search

knowledge-based and random selection procedures

task

approaches

models

morphologically rich languages

Arabic

Turkish

conditioning variables

morphological or syntactic features

model parameters

language modeling tasks

task

morphologically rich languages

factored language models

approaches

genetic search

entirely data-driven model selection procedure

knowledge-based and random selection procedures

language modeling tasks

morphological or syntactic features

conditioning variables

Arabic

language modeling tasks

Turkish

language modeling tasks

entirely data-driven model selection procedure

knowledge-based and random selection procedures

Arabic

Turkish


Input: We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. We obtained reasonable classification accuracy for all dimensions. Dialogue strategies based on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users.

user modeling

user model

user models

decision tree learning

Dialogue strategies

user modeling

Kyoto city bus information system

cooperative responses

cooperative responses

dialogue duration

spoken dialogue systems

studies

models

system

real dialogue data

classification accuracy

user modeling

cooperative responses

decision tree learning

models

real dialogue data

decision tree learning

system

real dialogue data

Dialogue strategies

Kyoto city bus information system

user modeling

Dialogue strategies

user modeling

spoken dialogue systems

studies

user model


Input: This paper proposes a novel method of building polarity-tagged corpus from HTML documents. The characteristics of this method is that it is fully automatic and can be applied to arbitrary HTML documents. The idea behind our method is to utilize certain layout structures and linguistic pattern. By using them, we can automatically extract such sentences that express opinion. In our experiment, the method could construct a corpus consisting of 126,610 sentences.

method

method

it

method

them

method

building polarity-tagged corpus

HTML documents

HTML documents

layout structures

linguistic pattern

method

building polarity-tagged corpus

HTML documents

method

it

HTML documents

layout structures

method

linguistic pattern

method

layout structures

linguistic pattern


Input: Previous work has used monolingual parallel corpora to extract and generate paraphrases. We show that this task can be done using bilingual parallel corpora, a much more commonly available resource. Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. We evaluate our paraphrase extraction and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.

monolingual parallel corpora

bilingual parallel corpora

bilingual parallel corpus

manual word alignments

paraphrases

paraphrases

paraphrase probability

paraphrases

translation probabilities

contextual information

paraphrases

automatic alignments

task

it

alignment techniques

paraphrase extraction and ranking methods

phrase-based statistical machine translation

quality

monolingual parallel corpora

paraphrases

bilingual parallel corpora

task

alignment techniques

phrase-based statistical machine translation

translation probabilities

paraphrases

contextual information

it

paraphrases

bilingual parallel corpus

paraphrases

automatic alignments

manual word alignments

paraphrase extraction and ranking methods

quality

paraphrases


Input: This paper proposes an automatic, essentially domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB- which combines software we have developed for that purpose -LRB- the'' Comparator'' -RRB- and a set of specifications for answer expressions -LRB- the'' Common Answer Specification'', or CAS -RRB-. The Comparator checks whether the answer provided by a SLS accords with a canonical answer, returning either true or false. The Common Answer Specification determines the syntax of answer expressions, the minimal content that must be included in them, the data to be included in and excluded from test corpora, and the procedures used by the Comparator. Though some details of the CAS are particular to individual domains, the Comparator software is domain-independent, as is the CAS approach.

domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB-

Comparator

Common Answer Specification

CAS

Comparator

SLS

Common Answer Specification

Comparator

Comparator software

CAS approach

software

specifications

answer expressions

syntax of answer expressions

CAS

software

domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB-

specifications

domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB-

software

specifications

specifications

answer expressions

Common Answer Specification

syntax of answer expressions


Input: This paper describes an unsupervised learning method for associative relationships between verb phrases, which is important in developing reliable Q&A systems. Consider the situation that a user gives a query ``How much petrol was imported to Japan from Saudi Arabia?'' to a Q&A system, but the text given to the system includes only the description ``X tonnes of petrol was conveyed to Japan from Saudi Arabia''. We think that the description is a good clue to find the answer for our query, ``X tonnes''. But there is no large-scale database that provides the associative relationship between ``imported'' and ``conveyed''. Our aim is to develop an unsupervised learning method that can obtain such an associative relationship, which we call scenario consistency. The method we are currently working on uses an expectation-maximization -LRB- EM -RRB- based word-clustering algorithm, and we have evaluated the effectiveness of this method using Japanese verb phrases.

unsupervised learning method

unsupervised learning method

expectation-maximization -LRB- EM -RRB- based word-clustering algorithm

associative relationships between verb phrases

associative relationship

associative relationship

scenario consistency

Q&A systems

Q&A system

large-scale database

Japanese verb phrases

method

method

unsupervised learning method

associative relationships between verb phrases

associative relationships between verb phrases

Q&A systems

unsupervised learning method

associative relationship

expectation-maximization -LRB- EM -RRB- based word-clustering algorithm

method

Japanese verb phrases

method


Input: We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages. We scrape text from multiple genres including blogs, online news, translated TED talks, and subtitles. Using linearly interpolated language models, we find that blogs and movie subtitles are more relevant for language modeling of conversational telephone speech and obtain large reductions in out-of-vocabulary keywords. Furthermore, we show that the web data can improve Term Error Rate Performance by 3.8% absolute and Maximum Term-Weighted Value in Keyword Search by 0.0076-0 .1059 absolute points. Much of the gain comes from the reduction of out-of-vocabulary items.

text data

web

Low Resource Languages

blogs

online news

translated TED talks

subtitles

blogs

movie subtitles

web data

language models

linearly interpolated language models

language modeling of conversational telephone speech

Automatic Speech Recognition

Keyword Search

Keyword Search

genres

out-of-vocabulary keywords

reduction of out-of-vocabulary items

Term Error Rate Performance

Maximum Term-Weighted Value

text data

language models

language models

Automatic Speech Recognition

language models

Keyword Search

Low Resource Languages

Automatic Speech Recognition

Low Resource Languages

Keyword Search

linearly interpolated language models

language modeling of conversational telephone speech

blogs

language modeling of conversational telephone speech

movie subtitles

language modeling of conversational telephone speech

web data

Keyword Search

web

text data

Automatic Speech Recognition

Keyword Search

blogs

online news

online news

translated TED talks

translated TED talks

subtitles

blogs

movie subtitles

blogs

genres

online news

genres

translated TED talks

genres

subtitles

genres

Term Error Rate Performance

Keyword Search

Maximum Term-Weighted Value

Keyword Search


Input: Pipelined Natural Language Generation -LRB- NLG -RRB- systems have grown increasingly complex as architectural modules were added to support language functionalities such as referring expressions, lexical choice, and revision. This has given rise to discussions about the relative placement of these new modules in the overall architecture. Recent work on another aspect of multi-paragraph text, discourse markers, indicates it is time to consider where a discourse marker insertion algorithm fits in. We present examples which suggest that in a pipelined NLG architecture, the best approach is to strongly tie it to a revision component. Finally, we evaluate the approach in a working multi-page system.

Pipelined Natural Language Generation -LRB- NLG -RRB- systems

architectural modules

discourse marker insertion algorithm

pipelined NLG architecture

revision component

language functionalities

referring expressions

lexical choice

revision

discourse markers

modules

overall architecture

approach

it

approach

multi-paragraph text

multi-page system

architectural modules

language functionalities

referring expressions

language functionalities

lexical choice

language functionalities

revision

language functionalities

referring expressions

lexical choice

lexical choice

revision

it

revision component

modules

overall architecture

revision component

pipelined NLG architecture

multi-page system

approach


Input: In this paper a system which understands and conceptualizes scenes descriptions in natural language is presented. Specifically, the following components of the system are described: the syntactic analyzer, based on a Procedural Systemic Grammar, the semantic analyzer relying on the Conceptual Dependency Theory, and the dictionary.

system

components

system

scenes descriptions in natural language

syntactic analyzer

Procedural Systemic Grammar

semantic analyzer

Conceptual Dependency Theory

dictionary

system

scenes descriptions in natural language

Procedural Systemic Grammar

syntactic analyzer

Conceptual Dependency Theory

semantic analyzer

components

system

syntactic analyzer

components

semantic analyzer

components

dictionary

components

syntactic analyzer

semantic analyzer

semantic analyzer

dictionary


Input: This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. -LRB- 1998 -RRB-. We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline model -LRB- that of Collins -LSB- 1999 -RSB- -RRB- with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model's score of 88.2%. The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear -LRB- maximum-entropy -RRB- models. Although the experiments in this article are on natural language parsing -LRB- NLP -RRB-, the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.

approaches

parses

model

approach

it

method

method

model

model

baseline model

algorithm

algorithm

method

probabilistic parser

parser

generative model

boosting approach

boosting method

baseline model

boosting approach

boosting approach

feature selection methods

log-linear -LRB- maximum-entropy -RRB- models

candidate parses

ranking

ranking

features

tree

tree

features

features

derivation

features

log-likelihood

features

parse trees

sparsity of the feature space

reranking task

ranking problems

parsing

natural language parsing -LRB- NLP -RRB-

NLP problems

ranking tasks

speech recognition

machine translation

natural language generation

Wall Street Journal treebank

parsing data

F-measure

F-measure

ranking

parses

sparsity of the feature space

parsing data

model

ranking

features

model

features

generative model

method

reranking task

boosting approach

method

boosting approach

ranking problems

boosting method

parsing

Wall Street Journal treebank

boosting method

algorithm

boosting approach

sparsity of the feature space

algorithm

ranking tasks

NLP problems

log-likelihood

method

feature selection methods

log-linear -LRB- maximum-entropy -RRB- models

log-likelihood

baseline model

speech recognition

machine translation

machine translation

natural language generation

F-measure

model

F-measure

baseline model

baseline model

model

algorithm

boosting approach

speech recognition

NLP problems

speech recognition

ranking tasks

machine translation

NLP problems

machine translation

ranking tasks

natural language generation

NLP problems

natural language generation

ranking tasks


Input: A model is presented to characterize the class of languages obtained by adding reduplication to context-free languages. The model is a pushdown automaton augmented with the ability to check reduplication by using the stack in a new way. The class of languages generated is shown to lie strictly between the context-free languages and the indexed languages. The model appears capable of accommodating the sort of reduplications that have been observed to occur in natural languages, but it excludes many of the unnatural constructions that other formal models have permitted.

model

model

it

class of languages

reduplication

reduplication

stack

class of languages

reduplications

context-free languages

context-free languages

indexed languages

natural languages

model

pushdown automaton

formal models

model

class of languages

reduplication

context-free languages

stack

pushdown automaton

stack

reduplication

model

reduplications

model

pushdown automaton

context-free languages

indexed languages


Input: We present an image set classification algorithm based on unsupervised clustering of labeled training and unla-beled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grass-mannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.

image set classification algorithm

unsupervised clustering

iterative sparse spectral clustering algorithm

Grass-mannian manifolds

spectral clustering algorithm

eigenvector solver

spectral clustering

labeled training and unla-beled test data

Image sets

datasets

stopping criterion

probability distribution

clusters

proximity matrix

local subspace structure

Initial clusters

global data structure

finer clusters

subtle class differences

global scale

Euclidean space

set based similarity measure

computational cost

clustering quality

classification results

algorithm

unsupervised clustering

image set classification algorithm

labeled training and unla-beled test data

unsupervised clustering

probability distribution

set based similarity measure

proximity matrix

local subspace structure

Initial clusters

global data structure

finer clusters

subtle class differences

Grass-mannian manifolds

Image sets

spectral clustering algorithm

Euclidean space

eigenvector solver

spectral clustering

computational cost

spectral clustering

clustering quality

spectral clustering

classification results

spectral clustering


Input: This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation. These models can be viewed as pairs of probabilistic context-free grammars working in a `synchronous' way. Two hardness results for the class NP are reported, along with an exponential time lower-bound for certain classes of algorithms that are currently used in the literature.

computational problems

exponential time lower-bound

probabilistic translation models

probabilistic context-free grammars

machine translation

models

computational problems

probabilistic translation models

probabilistic context-free grammars

models

probabilistic translation models

machine translation


Input: Active shape models are a powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a pri-ori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of build-ing/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

Active shape models

models of shape variation

search algorithms

PCA

non-linear extensions of active shape models

active shape models

minimum description length principle

linear modeling

complex image data

synthetic data

medical images

hand contours

pri-ori knowledge

linearity

non-linearities

rotations

user interaction

modes of variations

build-ing/choosing optimal models

algorithm

model

method

Active shape models

complex image data

search algorithms

models of shape variation

pri-ori knowledge

search algorithms

user interaction

non-linear extensions of active shape models

minimum description length principle

algorithm

linearity

PCA

modes of variations

model

rotations

non-linearities

synthetic data

method

medical images

method

hand contours

method

synthetic data

medical images

medical images

hand contours


Input: We describe a set of experiments to explore statistical techniques for ranking and selecting the best translations in a graph of translation hypotheses. In a previous paper -LRB- Carl, 2007 -RRB- we have described how the hypotheses graph is generated through shallow mapping and permutation rules. We have given examples of its nodes consisting of vectors representing morpho-syntactic properties of words and phrases. This paper describes a number of methods for elaborating statistical feature functions from some of the vector components. The feature functions are trained off-line on different types of text and their log-linear combination is then used to retrieve the best M translation paths in the graph. We compare two language modelling toolkits, the CMU and the SRI toolkit and arrive at three results: 1 -RRB- word-lemma based feature function models produce better results than token-based models, 2 -RRB- adding a PoS-tag feature function to the word-lemma model improves the output and 3 -RRB- weights for lexical translations are suitable if the training material is similar to the texts to be translated.

statistical techniques

shallow mapping

permutation rules

vector components

language modelling toolkits

CMU and the SRI toolkit

word-lemma based feature function models

token-based models

word-lemma model

ranking

lexical translations

graph of translation hypotheses

hypotheses graph

nodes

vectors representing morpho-syntactic properties

statistical feature functions

feature functions

log-linear combination

translation paths

graph

PoS-tag feature function

methods

statistical techniques

ranking

shallow mapping

hypotheses graph

permutation rules

hypotheses graph

methods

statistical feature functions

vector components

methods

log-linear combination

translation paths

shallow mapping

permutation rules

translation paths

graph

PoS-tag feature function

word-lemma model

CMU and the SRI toolkit

language modelling toolkits

word-lemma based feature function models

token-based models


Input: This paper presents a specialized editor for a highly structured dictionary. The basic goal in building that editor was to provide an adequate tool to help lexicologists produce a valid and coherent dictionary on the basis of a linguistic theory. If we want valuable lexicons and grammars to achieve complex natural language processing, we must provide very powerful tools to help create and ensure the validity of such complex linguistic databases. Our most important task in building the editor was to define a set of coherence rules that could be computationally applied to ensure the validity of lexical entries. A customized interface for browsing and editing was also designed and implemented.

editor

editor

linguistic theory

editor

structured dictionary

linguistic databases

dictionary

natural language processing

coherence rules

structured dictionary

editor

editor

dictionary

linguistic theory

dictionary


Input: Existing techniques extract term candidates by looking for internal and contextual information associated with domain specific terms. The algorithms always face the dilemma that fewer features are not enough to distinguish terms from non-terms whereas more features lead to more conflicts among selected features. This paper presents a novel approach for term extraction based on delimiters which are much more stable and domain independent. The proposed approach is not as sensitive to term frequency as that of previous works. This approach has no strict limit or hard rules and thus they can deal with all kinds of terms. It also requires no prior domain knowledge and no additional training to adapt to new domains. Consequently, the proposed approach can be applied to different domains easily and it is especially useful for resource-limited domains. Evaluations conducted on two different domains for Chinese term extraction show significant improvements over existing techniques which verifies its efficiency and domain independent nature. Experiments on new term extraction indicate that the proposed approach can also serve as an effective tool for domain lexicon expansion.

internal and contextual information

domain specific terms

features

features

delimiters

term frequency

hard rules

domain knowledge

approach

approach

approach

approach

it

Evaluations

approach

tool

term extraction

Chinese term extraction

new term extraction

domain lexicon expansion

resource-limited domains

domain specific terms

internal and contextual information

approach

term extraction

delimiters

approach

it

resource-limited domains

approach

domain lexicon expansion

approach

term frequency

Evaluations

Chinese term extraction


Input: We describe a method for identifying systematic patterns in translation data using part-of-speech tag sequences. We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output.

method

analysis

application

systematic patterns in translation data

patterns in machine translation output

part-of-speech tag sequences

diagnostic tool

machine translation systems

method

systematic patterns in translation data

part-of-speech tag sequences

method

diagnostic tool

machine translation systems

application

patterns in machine translation output

analysis

diagnostic tool


Input: We study the number of hidden layers required by a multilayer neu-ral network with threshold units to compute a function f from n d to -LCB- O, I -RCB-. In dimension d = 2, Gibson characterized the functions computable with just one hidden layer, under the assumption that there is no ``multiple intersection point'' and that f is only defined on a compact set. We consider the restriction of f to the neighborhood of a multiple intersection point or of infinity, and give necessary and sufficient conditions for it to be locally computable with one hidden layer. We show that adding these conditions to Gib-son's assumptions is not sufficient to ensure global computability with one hidden layer, by exhibiting a new non-local configuration, the ``critical cycle'', which implies that f is not computable with one hidden layer.

number of hidden layers

threshold units

hidden layer

hidden layer

global computability

hidden layer

non-local configuration

``critical cycle''

hidden layer

multilayer neu-ral network

number of hidden layers

multilayer neu-ral network

threshold units

number of hidden layers

``critical cycle''

non-local configuration


Input: This paper presents an approach to estimate the intrinsic texture properties -LRB- albedo, shading, normal -RRB- of scenes from multiple view acquisition under unknown illumination conditions. We introduce the concept of intrinsic textures, which are pixel-resolution surface textures representing the intrinsic appearance parameters of a scene. Unlike previous video relighting methods, the approach does not assume regions of uniform albedo, which makes it applicable to richly textured scenes. We show that intrinsic image methods can be used to refine an initial, low-frequency shading estimate based on a global lighting reconstruction from an original texture and coarse scene geometry in order to resolve the inherent global ambiguity in shading. The method is applied to relight-ing of free-viewpoint rendering from multiple view video capture. This demonstrates relighting with reproduction of fine surface detail.

approach

approach

it

method

intrinsic texture properties -LRB- albedo, shading, normal -RRB- of scenes

multiple view acquisition

initial, low-frequency shading estimate

relight-ing of free-viewpoint rendering

relighting

unknown illumination conditions

intrinsic textures

pixel-resolution surface textures

intrinsic appearance parameters

uniform albedo

global lighting reconstruction

texture and coarse scene geometry

inherent global ambiguity in shading

multiple view video capture

reproduction of fine surface detail

video relighting methods

intrinsic image methods

richly textured scenes

approach

intrinsic texture properties -LRB- albedo, shading, normal -RRB- of scenes

multiple view acquisition

intrinsic texture properties -LRB- albedo, shading, normal -RRB- of scenes

it

richly textured scenes

intrinsic image methods

initial, low-frequency shading estimate

initial, low-frequency shading estimate

inherent global ambiguity in shading

global lighting reconstruction

intrinsic image methods

method

relight-ing of free-viewpoint rendering

multiple view video capture

relight-ing of free-viewpoint rendering

unknown illumination conditions

multiple view acquisition

texture and coarse scene geometry

global lighting reconstruction

reproduction of fine surface detail

relighting

approach

video relighting methods


Input: Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called POURPRE, for automatically evaluating answers to definition questions. Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system's response. The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003 and TREC 2004 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that POURPRE outperforms direct application of existing metrics.

automatic evaluation

automatic evaluation of machine translation and document summarization

machine translation

document summarization

automatically evaluating answers to definition questions

approach

measure

metric

metrics

POURPRE

POURPRE

TREC 2003 and TREC 2004 QA tracks

rankings

official rankings

automatic evaluation

machine translation

automatic evaluation

document summarization

TREC 2003 and TREC 2004 QA tracks

metric

TREC 2003 and TREC 2004 QA tracks

POURPRE

TREC 2003 and TREC 2004 QA tracks

metrics

machine translation

document summarization

measure

approach

measure

automatically evaluating answers to definition questions

metric

rankings

POURPRE

metrics


Input: Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach. However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.

Automatic Speech Recognition technology

hand-crafting knowledge-based generation systems

machine learning techniques

dialog systems

speech recognition

dialog systems

natural language generation community

dialog systems

generation

dialog systems

they

system response

Automatic Speech Recognition technology

dialog systems

generation

dialog systems

machine learning techniques

hand-crafting knowledge-based generation systems

system response

natural language generation community

natural language generation community

dialog systems


Input: We present a tool, called ILIMP, which takes as input a raw text in French and produces as output the same text in which every occurrence of the pronoun il is tagged either with tag -LSB- ANA -RSB- for anaphoric or -LSB- IMP -RSB- for impersonal or expletive. This tool is therefore designed to distinguish between the anaphoric occurrences of il, for which an anaphora resolution system has to look for an antecedent, and the expletive occurrences of this pronoun, for which it does not make sense to look for an antecedent. The precision rate for ILIMP is 97,5%. The few errors are analyzed in detail. Other tasks using the method developed for ILIMP are described briefly, as well as the use of ILIMP in a modular syntactic analysis system.

tool

tool

tasks

method

ILIMP

anaphora resolution system

ILIMP

ILIMP

ILIMP

modular syntactic analysis system

raw text in French

anaphoric occurrences of il

precision rate

raw text in French

tool

tool

anaphoric occurrences of il

anaphora resolution system

anaphoric occurrences of il

method

tasks

method

ILIMP

ILIMP

modular syntactic analysis system

precision rate

ILIMP


Input: Speech-based depression detection has gained importance in recent years, but most research has used relatively quiet conditions or examined a single corpus per study. Little is thus known about the robustness of speech cues in the wild. This study compares the effect of noise and reverberation on depression prediction using 1 -RRB- standard mel-frequency cepstral coefficients -LRB- MFCCs -RRB-, and 2 -RRB- features designed for noise robustness, damped oscillator cepstral coefficients -LRB- DOCCs -RRB-. Data come from the 2014 AudioVisual Emotion Recognition Challenge -LRB- AVEC -RRB-. Results using additive noise and reverberation reveal a consistent pattern of findings for multiple evaluation metrics under both matched and mismatched conditions. First and most notably: standard MFCC features suffer dramatically under test/train mismatch for both noise and reverberation; DOCC features are far more robust. Second, including higher-order cepstral coefficients is generally beneficial. Third, artificial neural networks tend to outperform support vector regression. Fourth, spontaneous speech appears to offer better robustness than read speech. Finally, a cross-corpus -LRB- and cross-language -RRB- experiment reveals better noise and reverberation robustness for DOCCs than for MFCCs. Implications and future directions for real-world robust depression detection are discussed.

Speech-based depression detection

depression prediction

real-world robust depression detection

robustness

noise robustness

evaluation metrics

noise and reverberation robustness

speech cues

noise

reverberation

additive noise

reverberation

noise

reverberation

higher-order cepstral coefficients

cross-corpus -LRB- and cross-language -RRB- experiment

mel-frequency cepstral coefficients -LRB- MFCCs -RRB-

damped oscillator cepstral coefficients -LRB- DOCCs -RRB-

MFCC features

DOCC features

artificial neural networks

support vector regression

DOCCs

MFCCs

features

AudioVisual Emotion Recognition Challenge -LRB- AVEC -RRB-

spontaneous speech

read speech

speech cues

robustness

noise

depression prediction

reverberation

depression prediction

noise

reverberation

mel-frequency cepstral coefficients -LRB- MFCCs -RRB-

features

features

damped oscillator cepstral coefficients -LRB- DOCCs -RRB-

additive noise

reverberation

noise

reverberation

mel-frequency cepstral coefficients -LRB- MFCCs -RRB-

depression prediction

features

noise robustness

DOCC features

MFCC features

artificial neural networks

support vector regression

spontaneous speech

read speech

DOCCs

MFCCs

cross-corpus -LRB- and cross-language -RRB- experiment

DOCCs

cross-corpus -LRB- and cross-language -RRB- experiment

MFCCs

noise and reverberation robustness

DOCCs

noise and reverberation robustness

MFCCs


Input: This paper proposes document oriented preference sets -LRB- DoPS -RRB- for the disambiguation of the dependency structure of sentences. The DoPS system extracts preference knowledge from a target document or other documents automatically. Sentence ambiguities can be resolved by using domain targeted preference knowledge without using complicated large knowledgebases. Implementation and empirical results are described for the the analysis of dependency structures of Japanese patent claim sentences.

document oriented preference sets -LRB- DoPS -RRB-

DoPS system

disambiguation of the dependency structure

Sentence ambiguities

domain targeted preference knowledge

dependency structures

knowledgebases

Japanese patent claim sentences

document oriented preference sets -LRB- DoPS -RRB-

disambiguation of the dependency structure

domain targeted preference knowledge

Sentence ambiguities

domain targeted preference knowledge

knowledgebases

dependency structures

Japanese patent claim sentences


Input: Multimodal interfaces require effective parsing and understanding of utterances whose content is distributed across multiple input modes. Johnston 1998 presents an approach in which strategies for multimodal integration are stated declaratively using a unification-based grammar that is used by a multidimensional chart parser to compose inputs. This approach is highly expressive and supports a broad class of interfaces, but offers only limited potential for mutual compensation among the input modes, is subject to significant concerns in terms of computational complexity, and complicates selection among alternative multimodal interpretations of the input. In this paper, we present an alternative approach in which multimodal parsing and understanding are achieved using a weighted finite-state device which takes speech and gesture streams as inputs and outputs their joint interpretation. This approach is significantly more efficient, enables tight-coupling of multimodal understanding with speech recognition, and provides a general probabilistic framework for multimodal ambiguity resolution.

Multimodal interfaces

multimodal integration

multimodal parsing and understanding

multimodal understanding

speech recognition

multimodal ambiguity resolution

parsing

unification-based grammar

multidimensional chart parser

weighted finite-state device

approach

approach

approach

approach

interfaces

computational complexity

speech and gesture streams

parsing

Multimodal interfaces

approach

multimodal integration

unification-based grammar

multimodal integration

unification-based grammar

multidimensional chart parser

approach

multimodal parsing and understanding

weighted finite-state device

multimodal parsing and understanding

speech and gesture streams

weighted finite-state device

approach

multimodal ambiguity resolution

speech recognition

multimodal understanding


Input: Recently, we initiated a project to develop a phonetically-based spoken language understanding system called SUMMIT. In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering, our approach attempts to express the speech knowledge within a formal framework using well-defined mathematical tools. In our system, features and decision strategies are discovered and trained automatically, using a large body of speech data. This paper describes the system, and documents its current performance.

phonetically-based spoken language understanding system

SUMMIT

heuristic rules

knowledge engineering

mathematical tools

decision strategies

approach

system

system

speech knowledge

features

speech data

SUMMIT

phonetically-based spoken language understanding system

knowledge engineering

heuristic rules

approach

speech knowledge

mathematical tools

speech knowledge

speech data

decision strategies

features

decision strategies


Input: This paper describes an implemented program that takes a tagged text corpus and generates a partial list of the subcategorization frames in which each verb occurs. The completeness of the output list increases monotonically with the total occurrences of each verb in the training corpus. False positive rates are one to three percent. Five subcategorization frames are currently detected and we foresee no impediment to detecting many more. Ultimately, we expect to provide a large subcategorization dictionary to the NLP community and to train dictionaries for specific corpora.

program

tagged text corpus

subcategorization dictionary

subcategorization frames

subcategorization frames

False positive rates

tagged text corpus

program


Input: We present a method for estimating the relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras from observing a moving object. We show how to tackle the problem of missing point correspondences heavily required by SfM pipelines and how to go beyond this basic paradigm. We relax the non-linear nature of the problem by accepting two assumptions which surveillance scenarios offer, ie. the presence of a moving object and easily estimable gravity vector. By those assumptions we cast the problem as a Quadratic Eigenvalue Problem offering an elegant way of treating nonlinear monomials and delivering a quasi closed-form solution as a reliable starting point for a further bundle adjustment. We are the first to bring the closed form solution to such a very practical problem arising in video surveillance. Results in different camera setups demonstrate the feasibility of the approach.

method

paradigm

problem

assumptions

assumptions

problem

problem

approach

relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras

missing point correspondences

Quadratic Eigenvalue Problem

bundle adjustment

video surveillance

SfM pipelines

quasi closed-form solution

closed form solution

non-linear nature

surveillance scenarios

gravity vector

nonlinear monomials

method

relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras

SfM pipelines

missing point correspondences

Quadratic Eigenvalue Problem

problem

Quadratic Eigenvalue Problem

nonlinear monomials

Quadratic Eigenvalue Problem

quasi closed-form solution

quasi closed-form solution

bundle adjustment

closed form solution

problem

non-linear nature

problem

video surveillance

problem


Input: In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear Support Vector Machine -LRB- SVM -RRB- classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known Motion History Image -LRB- MHI -RRB- and propose a new Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.

human action recognition system

linear Support Vector Machine -LRB- SVM -RRB- classifier

classification progress

Motion History Image -LRB- MHI -RRB-

Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature

HMHH

MHI

HMHH

SVM classifiers

embedded computer vision applications

security systems

human-computer interaction

intelligent environments

embedded computer vision application

recognition

system

system

system

embedded hardware

compacted motion features

motion information

rich motion information

low dimension feature vector

videos

human action recognition system

embedded computer vision applications

embedded computer vision applications

security systems

embedded computer vision applications

human-computer interaction

embedded computer vision applications

intelligent environments

system

embedded computer vision application

linear Support Vector Machine -LRB- SVM -RRB- classifier

system

embedded hardware

classification progress

videos

compacted motion features

Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature

motion information

HMHH

rich motion information

MHI

low dimension feature vector

HMHH

low dimension feature vector

low dimension feature vector

SVM classifiers

security systems

human-computer interaction

human-computer interaction

intelligent environments

MHI

HMHH

recognition

system


Input: In this paper I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process. It will be shown that this view supports flexible and efficient natural language processing.

model of grammatical processing

uniform processing

parametrized deduction process

knowledge sources

model

tasks

parsing

generation

natural language processing

uniform processing

model of grammatical processing

knowledge sources

model of grammatical processing

parametrized deduction process

tasks

knowledge sources

uniform processing

parsing

generation

parsing

tasks

generation

tasks


Input: Link detection has been regarded as a core technology for the Topic Detection and Tracking tasks of new event detection. In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems. Motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists. Experimental results validate our hypothesis.

Link detection

Topic Detection and Tracking tasks of new event detection

story link detection

new event detection

information retrieval task

precision

recall

systems

performance enhancing techniques

part of speech tagging

similarity measures

expanded stop lists

Link detection

Topic Detection and Tracking tasks of new event detection

information retrieval task

story link detection

information retrieval task

new event detection

story link detection

new event detection

precision

recall

part of speech tagging

similarity measures

similarity measures

expanded stop lists

story link detection

systems

new event detection

systems

precision

systems

recall

systems

part of speech tagging

performance enhancing techniques

similarity measures

performance enhancing techniques

expanded stop lists

performance enhancing techniques


Input: We attempt to understand visual classification in humans using both psy-chophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classified the faces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and flowfield representation of the faces. The classification performance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated by the subjects. We then correlated the human responses to the distance of the stimuli to the separating hyperplane of the learning algorithms. Our results suggest that human classification can be modeled by some hyperplane algorithms in the feature space we used. For classification, the brain needs more processing for stimuli close to that hyperplane than for those further away.

visual classification

gender classification task

classification task

classification

face database

human classification

classification

psy-chophysical and machine learning techniques

hyperplane learning algorithms

flowfield representation of the faces

learning algorithms

learning algorithms

hyperplane algorithms

Frontal views of human faces

gender judgment

reaction time

confidence rating

Principal Components of the texture

hyperplane of the learning algorithms

feature space

hyperplane

those

psy-chophysical and machine learning techniques

visual classification

Frontal views of human faces

gender classification task

hyperplane learning algorithms

classification task

Principal Components of the texture

hyperplane learning algorithms

flowfield representation of the faces

hyperplane learning algorithms

learning algorithms

classification

hyperplane algorithms

human classification

flowfield representation of the faces

Principal Components of the texture

face database

learning algorithms

feature space

hyperplane algorithms

hyperplane

those


Input: In this paper, we present a corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system for Dutch which combines statistical classification -LRB- maximum entropy -RRB- with linguistic information. Instead of building individual classifiers per ambiguous wordform, we introduce a lemma-based approach. The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in one classifier, therefore augmenting the training material available to the algorithm. Testing the lemma-based model on the Dutch Senseval-2 test data, we achieve a significant increase in accuracy over the wordform model. Also, the WSD system based on lemmas is smaller and more robust.

corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system

statistical classification

maximum entropy

classifiers

lemma-based approach

classifier

lemma-based model

wordform model

WSD system based on lemmas

Dutch

Dutch Senseval-2 test data

linguistic information

ambiguous wordform

inflected forms

ambiguous word

method

it

algorithm

corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system

Dutch

ambiguous wordform

classifiers

statistical classification

corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system

maximum entropy

corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system

linguistic information

corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system

linguistic information

maximum entropy

classifiers

lemma-based approach

lemma-based model

wordform model

inflected forms

ambiguous word

Dutch Senseval-2 test data

lemma-based model


Input: We propose an exact, general and efficient coarse-to-fine energy minimization strategy for semantic video segmenta-tion. Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs. The strategy is exact, i.e., it produces the same solution as minimizing over the finest graph. It is general, i.e., it can be used to minimize any energy function -LRB- e.g., unary, pairwise, and higher-order terms -RRB- with any existing energy minimization algorithm -LRB- e.g., graph cuts and belief propagation -RRB-. It also gives significant speedups in inference for several datasets with varying degrees of spatio-temporal continuity. We also discuss the strengths and weaknesses of our strategy relative to existing hierarchical approaches, and the kinds of image and video data that provide the best speedups.

coarse-to-fine energy minimization strategy

energy minimization algorithm

graph cuts

belief propagation

hierarchical approaches

semantic video segmenta-tion

hierarchical abstraction of the supervoxel graph

inference

strategy

strategy

it

It

it

It

datasets

strategy

hierarchy

coarser graphs

finest graph

energy function

spatio-temporal continuity

image and video data

coarse-to-fine energy minimization strategy

semantic video segmenta-tion

hierarchical abstraction of the supervoxel graph

strategy

it

energy function

energy minimization algorithm

energy function

It

inference

it

energy minimization algorithm

graph cuts

belief propagation

graph cuts

energy minimization algorithm

belief propagation

energy minimization algorithm

datasets

It

spatio-temporal continuity

datasets

strategy

hierarchical approaches


Input: Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques. We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.

ensemble methods

multi-strategy and multi-source approach

answering agents

answering agents

knowledge-based mechanisms

statistical techniques

multi-level answer resolution algorithm

answering agents

answer resolution algorithm

machine learning

natural language processing

question answering

strategies

one

other

baseline system

question, passage, and/or answer levels

average precision metric

ensemble methods

machine learning

ensemble methods

natural language processing

multi-strategy and multi-source approach

question answering

strategies

answering agents

knowledge-based mechanisms

one

statistical techniques

other

answering agents

multi-level answer resolution algorithm

one

strategies

other

strategies

answer resolution algorithm

baseline system

average precision metric

answer resolution algorithm

average precision metric

baseline system


Input: Word Identification has been an important and active issue in Chinese Natural Language Processing. In this paper, a new mechanism, based on the concept of sublanguage, is proposed for identifying unknown words, especially personal names, in Chinese newspapers. The proposed mechanism includes title-driven name recognition, adaptive dynamic word formation, identification of 2-character and 3-character Chinese names without title. We will show the experimental results for two corpora and compare them with the results by the NTHU's statistic-based system, the only system that we know has attacked the same problem. The experimental results have shown significant improvements over the WI systems without the name identification capability.

Word Identification

Chinese Natural Language Processing

title-driven name recognition

adaptive dynamic word formation

identification of 2-character and 3-character Chinese names without title

name identification

mechanism

mechanism

system

sublanguage

unknown words

personal names

Chinese newspapers

NTHU's statistic-based system

WI systems

Word Identification

Chinese Natural Language Processing

personal names

unknown words

mechanism

unknown words

sublanguage

mechanism

Chinese newspapers

mechanism

title-driven name recognition

mechanism

adaptive dynamic word formation

mechanism

identification of 2-character and 3-character Chinese names without title

mechanism

title-driven name recognition

adaptive dynamic word formation

adaptive dynamic word formation

identification of 2-character and 3-character Chinese names without title


Input: In this paper we summarize the experiences gained from a field trial of a speaker verification system. In the test implementation access to two rooms at the University of Frankfurt had been controlled by a speaker verification system. The paper is organized as follows: Firstly, we will describe the system concepts and implementation issues. Secondly, results of the user evaluation are reported. During the field trial all speech data was recorded. The data base created in this way has been used extensively for simulation experiments. In chapter 4 we will describe recent experiments focusing on the use of Hidden Markov Models.

speaker verification system

speaker verification system

user evaluation

Hidden Markov Models

speech data


Input: This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. Specifically, this system is designed to deterministically choose between pronominalization, superordinate substitution, and definite noun phrase reiteration. The system identifies a strength of antecedence recovery for each of the lexical substitutions.

Paul

computer text generation system

cohesive text

lexical substitutions

pronominalization

superordinate substitution

definite noun phrase reiteration

lexical substitutions

system

system

antecedence recovery

Paul

computer text generation system

computer text generation system

cohesive text

lexical substitutions

Paul

system

antecedence recovery

antecedence recovery

lexical substitutions

pronominalization

superordinate substitution

superordinate substitution

definite noun phrase reiteration


Input: This paper addresses the problem of identifying likely topics of texts by their position in the text. It describes the automated training and evaluation of an Optimal Position Policy, a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure. This method can be used in applications such as information retrieval, routing, and text summarization.

Optimal Position Policy

method

method

applications

positions of topic-bearing sentences

genre-specific regularities of discourse structure

information retrieval

routing

text summarization

method

positions of topic-bearing sentences

genre-specific regularities of discourse structure

method

method

applications

information retrieval

applications

routing

applications

text summarization

applications

information retrieval

routing

routing

text summarization


Input: We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.

framework

framework

framework

framework

approach

it

methods

online multiclass learning

multiclass categorization

notion of hypothesis sharing

multiclass Perceptron

unifying mistake bound analysis

online learning process

synthetic and natural datasets

framework

online multiclass learning

notion of hypothesis sharing

framework

multiclass Perceptron

framework

it

methods

synthetic and natural datasets

it

synthetic and natural datasets

methods


Input: We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. We report on different aspects of the predictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables.

supervised machine learning

statistical models of WH-questions

models

models

shallow linguistic features of questions

user's informational goals

training and testing factors

supervised machine learning

statistical models of WH-questions

shallow linguistic features of questions

models


Input: We argue in favor of the the use of labeled directed graph to represent various types of linguistic structures, and illustrate how this allows one to view NLP tasks as graph transformations. We present a general method for learning such transformations from an annotated corpus and describe experiments with two applications of the method: identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB-.

labeled directed graph

method

linguistic structures

this

transformations

applications

method

NLP tasks

graph transformations

identification of non-local depenencies

semantic role labeling

annotated corpus

Penn Treebank data

Proposition Bank data

labeled directed graph

linguistic structures

labeled directed graph

NLP tasks

this

NLP tasks

method

transformations

annotated corpus

method

method

applications

Penn Treebank data

identification of non-local depenencies

Proposition Bank data

semantic role labeling

identification of non-local depenencies

applications

semantic role labeling

applications


Input: We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.

generative probabilistic model of natural language

HBG

HBG

decision tree building

linguistic introspection

robust probabilistic parsing models

P-CFG

HBG model

P-CFG

linguistic information

lexical, syntactic, semantic, and structural information

parse tree

parse tree

parse

parse

head-to-head tests

ambiguity

disambiguation process

grammar tailoring

corpus of bracketed sentences

Treebank

parsing accuracy rate

linguistic information

ambiguity

HBG

disambiguation process

lexical, syntactic, semantic, and structural information

HBG

corpus of bracketed sentences

parse tree

decision tree building

parse tree

parse tree

parse

grammar tailoring

parse

linguistic introspection

grammar tailoring

corpus of bracketed sentences

decision tree building

P-CFG

robust probabilistic parsing models

HBG model

P-CFG

parsing accuracy rate

HBG model


Input: The verb forms are often claimed to convey two kinds of information: 1. whether the event described in a sentence is present, past or future -LRB- = deictic information -RRB- 2. whether the event described in a sentence is presented as completed, going on, just starting or being finished -LRB- = aspectual information -RRB-. It will be demonstrated in this paper that one has to add a third component to the analysis of verb form meanings, namely whether or not they express habituality. The framework of the analysis is model-theoretic semantics.

verb forms

deictic information

aspectual information

model-theoretic semantics

information

analysis

analysis of verb form meanings

model-theoretic semantics

analysis


Input: This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experiments: one which ignored word-sense distinctions, resulting in 6.3% accuracy for semantic classification of verbs based on -LRB- Levin, 1993 -RRB-; and one which exploited word-sense distinctions, resulting in 97.9% accuracy. These experiments were dual purpose: -LRB- 1 -RRB- to validate the central thesis of the work of -LRB- Levin, 1993 -RRB-, i.e., that verb semantics and syntactic behavior are predictably related; -LRB- 2 -RRB- to demonstrate that a 15-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources.

word-sense ambiguity

construction of large-scale knowledge sources

machine-readable resources

online sources

word-sense distinctions

word-sense distinctions

verb semantics

syntactic behavior

semantic information

syntactic cues

syntactic cues

word senses

word senses

accuracy

accuracy

semantic classification

techniques

machine-readable resources

word-sense ambiguity

machine-readable resources

construction of large-scale knowledge sources

syntactic cues

semantic information

techniques

word senses

online sources

techniques

accuracy

semantic classification

verb semantics

syntactic behavior


Input: The TIPSTER Architecture has been designed to enable a variety of different text applications to use a set of common text processing modules. Since user interfaces work best when customized for particular applications, it is appropriator that no particular user interface styles or conventions are described in the TIPSTER Architecture specification. However, the Computing Research Laboratory -LRB- CRL -RRB- has constructed several TIPSTER applications that use a common set of configurable Graphical User Interface -LRB- GUI -RRB- functions. These GUIs were constructed using CRL's TIPSTER User Interface Toolkit -LRB- TUIT -RRB-. TUIT is a software library that can be used to construct multilingual TIPSTER user interfaces for a set of common user tasks. CRL developed TUIT to support their work to integrate TIPSTER modules for the 6 and 12 month TIPSTER II demonstrations as well as their Oleada and Temple demonstration projects. This paper briefly describes TUIT and its capabilities.

TIPSTER Architecture

common text processing modules

CRL's TIPSTER User Interface Toolkit -LRB- TUIT -RRB-

TUIT

software library

TUIT

TIPSTER modules

TUIT

text applications

TIPSTER applications

user interfaces

user interface styles or conventions

TIPSTER Architecture specification

Graphical User Interface -LRB- GUI -RRB- functions

GUIs

multilingual TIPSTER user interfaces

applications

TIPSTER Architecture

text applications

common text processing modules

text applications

user interfaces

applications

Graphical User Interface -LRB- GUI -RRB- functions

TIPSTER applications

CRL's TIPSTER User Interface Toolkit -LRB- TUIT -RRB-

GUIs

TUIT

multilingual TIPSTER user interfaces

TUIT

TIPSTER modules

TUIT

software library


Input: Current natural language interfaces have concentrated largely on determining the literal meaning of input from their users. While such decoding is an essential underpinning, much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication, such as robust communication procedures. This paper defends that view, but claims that direct imitation of human performance is not the best way to implement many of these non-literal aspects of communication; that the new technology of powerful personal computers with integral graphics displays offers techniques superior to those of humans for these aspects, while still satisfying human communication needs. The paper proposes interfaces based on a judicious mixture of these techniques and the still valuable methods of more traditional natural language interfaces.

natural language interfaces

natural language interfaces

personal computers

graphics displays

natural language interfaces

decoding

non-literal aspects of communication

robust communication procedures

non-literal aspects of communication

they

non-literal aspects of communication

they

graphics displays

personal computers

robust communication procedures

non-literal aspects of communication


Input: This paper proposes a framework in which Lagrangian Particle Dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a Flow Map, whose spatial gradients are subsequently used to setup a Cauchy Green Deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field, which reveals the Lagrangian Coherent Structures -LRB- LCS -RRB- present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.

Lagrangian Particle Dynamics

aperiodic dynamical system

numerical integration scheme

Flow Map

Cauchy Green Deformation tensor

normalized cuts framework

segmentation of high density crowd flows

detection of flow instabilities

flow field

moving crowd

grid of particles

flow field

evolution of particles

spatial gradients

maximum eigenvalue

Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field

Lagrangian Coherent Structures -LRB- LCS -RRB-

LCS

boundaries of the flow segments

tensor

Google Video

National Geographic documentary

Lagrangian Particle Dynamics

segmentation of high density crowd flows

Lagrangian Particle Dynamics

detection of flow instabilities

moving crowd

flow field

aperiodic dynamical system

flow field

grid of particles

flow field

numerical integration scheme

grid of particles

Flow Map

evolution of particles

spatial gradients

Cauchy Green Deformation tensor

maximum eigenvalue

Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field

Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field

Lagrangian Coherent Structures -LRB- LCS -RRB-

LCS

boundaries of the flow segments

normalized cuts framework

boundaries of the flow segments

segmentation of high density crowd flows

detection of flow instabilities

Google Video

National Geographic documentary

maximum eigenvalue

tensor


Input: In this paper we study a set of problems that are of considerable importance to Statistical Machine Translation -LRB- SMT -RRB- but which have not been addressed satisfactorily by the SMT research community. Over the last decade, a variety of SMT algorithms have been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT. Our work aims at providing useful insights into the the computational complexity of those problems. We prove that while IBM Models 1-2 are conceptually and computationally simple, computations involving the higher -LRB- and more useful -RRB- models are hard. Since it is unlikely that there exists a polynomial time solution for any of these hard problems -LRB- unless P = NP and P #P = P -RRB-, our results highlight and justify the need for developing polynomial time approximations for these computations. We also discuss some practical ways of dealing with complexity.

Statistical Machine Translation -LRB- SMT -RRB-

SMT

SMT algorithms

IBM Models 1-2

polynomial time solution

polynomial time approximations

computational complexity

computational complexity

complexity

problems

problems

computations

models

hard problems

computations

computational complexity

problems

computational complexity

problems

problems

SMT

IBM Models 1-2

models

polynomial time solution

hard problems

polynomial time approximations

computations


Input: Most state-of-the-art evaluation measures for machine translation assign high costs to movements of word blocks. In many cases though such movements still result in correct or almost correct sentences. In this paper, we will present a new evaluation measure which explicitly models block reordering as an edit operation. Our measure can be exactly calculated in quadratic time. Furthermore, we will show how some evaluation measures can be improved by the introduction of word-dependent substitution costs. The correlation of the new measure with human judgment has been investigated systematically on two different language pairs. The experimental results will show that it significantly outperforms state-of-the-art approaches in sentence-level correlation. Results from experiments with word dependent substitution costs will demonstrate an additional increase of correlation between automatic evaluation measures and human judgment.

evaluation measures

evaluation measure

evaluation measures

sentence-level correlation

automatic evaluation measures

machine translation

block reordering

edit operation

quadratic time

word-dependent substitution costs

human judgment

word dependent substitution costs

human judgment

measure

measure

it

approaches

language pairs

evaluation measures

machine translation

sentence-level correlation

it

sentence-level correlation

approaches

evaluation measure

block reordering

edit operation

block reordering

word-dependent substitution costs

evaluation measures

quadratic time

measure

measure

human judgment

it

approaches

automatic evaluation measures

human judgment


Input: The Rete and Treat algorithms are considered the most efficient implementation techniques for Forward Chaining rule systems. These algorithms support a language of limited expressive power. Assertions are not allowed to contain variables, making universal quantification impossible to express except as a rule. In this paper we show how to support full unification in these algorithms. We also show that: Supporting full unification is costly; Full unification is not used frequently; A combination of compile time and run time checks can determine when full unification is not needed. We present data to show that the cost of supporting full unification can be reduced in proportion to the degree that it isn't employed and that for many practical systems this cost is negligible.

Rete and Treat algorithms

implementation techniques

language of limited expressive power

Forward Chaining rule systems

full unification

full unification

Full unification

full unification

full unification

algorithms

algorithms

it

Assertions

variables

universal quantification

rule

compile time

run time

Rete and Treat algorithms

implementation techniques

Rete and Treat algorithms

Forward Chaining rule systems

algorithms

language of limited expressive power

algorithms

full unification

compile time

run time

compile time

full unification

run time

full unification


Input: A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs. Error correction is done by strongly biasing parsing toward expected meanings unless clear evidence from the input shows the current sentence is not expected. A dialogue acquisition and tracking algorithm is presented along with a description of its implementation in a voice interactive system. A series of tests are described that show the power of the error correction methodology when stereotypic dialogue occurs.

method

patterns

error correction

Error correction

parsing

ill-formed input

stereotypic dialogue

dialogue patterns

dialogue acquisition and tracking algorithm

voice interactive system

error correction methodology

method

error correction

ill-formed input

error correction

dialogue acquisition and tracking algorithm

voice interactive system

stereotypic dialogue

error correction methodology


Input: Traditional linear Fukunaga-Koontz Transform -LRB- FKT -RRB- -LSB- 1 -RSB- is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional -LRB- kernel -RRB- subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed Kernel Fukunaga-Koontz Transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.

linear Fukunaga-Koontz Transform -LRB- FKT -RRB-

discriminative subspaces building approach

FKT

linear FKT

Kernel Fukunaga-Koontz Transform

non-linear generalization

small-sample-size

higher dimensional -LRB- kernel -RRB- subspaces

discrimination ability

it

multi-class problem

face recognition applications

domain specific problems

linear Fukunaga-Koontz Transform -LRB- FKT -RRB-

discriminative subspaces building approach

FKT

small-sample-size

linear FKT

it

it

multi-class problem

it

higher dimensional -LRB- kernel -RRB- subspaces

non-linear generalization

domain specific problems

it

discrimination ability

multi-class problem

higher dimensional -LRB- kernel -RRB- subspaces

face recognition applications

Kernel Fukunaga-Koontz Transform


Input: The study addresses the problem of automatic acquisition of entailment relations between verbs. While this task has much in common with paraphrases acquisition which aims to discover semantic equivalence between verbs, the main challenge of entailment acquisition is to capture asymmetric, or directional, relations. Motivated by the intuition that it often underlies the local structure of coherent text, we develop a method that discovers verb entailment using evidence about discourse relations between clauses available in a parsed corpus. In comparison with earlier work, the proposed method covers a much wider range of verb entailment types and learns the mapping between verbs with highly varied argument structures.

automatic acquisition of entailment relations

paraphrases acquisition

entailment acquisition

verb entailment

mapping between verbs

task

method

method

semantic equivalence

asymmetric, or directional, relations

local structure of coherent text

discourse relations

verb entailment types

highly varied argument structures

parsed corpus

task

paraphrases acquisition

paraphrases acquisition

semantic equivalence

entailment acquisition

asymmetric, or directional, relations

method

verb entailment

discourse relations

method

parsed corpus

discourse relations

method

mapping between verbs

highly varied argument structures

mapping between verbs


Input: In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrödinger distance transform -LRB- SDT -RRB- representation. This is achieved by solving a static Schrödinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit L2 norm-making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm. We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques. Finally, to evaluate the performance of our algorithm-dubbed SDTM-we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques. The experiments show that our algorithm outperforms state-of-the-art point set registration algorithms on many quantitative metrics.

point cloud matching

shape matching problem

point cloud matching

point set matching

point clouds

static Schrödinger equation

static Hamilton-Jacobi equation

analytic expression

theoretical physics literature

square-root density

unit Hilbert sphere

intrinsic geometry

space of densities

analytic expressions

geodesic distance

rigid and non-rigid transformations

shape representation

Schrödinger distance transform -LRB- SDT -RRB- representation

SDT representation

Riemannian framework

matching algorithm

nonlinear optimization techniques

point set registration algorithms

it

natural metric

sphere

framework

transformations

state-of-the-art techniques

algorithm

Fisher-Rao metric

quantitative metrics

synthetic and real data examples

shape matching problem

point cloud matching

shape representation

point clouds

square-root density

it

natural metric

space of densities

analytic expressions

geodesic distance

Riemannian framework

point cloud matching

rigid and non-rigid transformations

point set matching

framework

point set matching

nonlinear optimization techniques

transformations

Schrödinger distance transform -LRB- SDT -RRB- representation

shape representation

SDT representation

analytic expression

intrinsic geometry

unit Hilbert sphere

algorithm

point set registration algorithms

quantitative metrics

algorithm

quantitative metrics

point set registration algorithms


Input: Using natural language processing, we carried out a trend survey on Japanese natural language processing studies that have been done over the last ten years. We determined the changes in the number of papers published for each research organization and on each research area as well as the relationship between research organizations and research areas. This paper is useful for both recognizing trends in Japanese NLP and constructing a method of supporting trend surveys using NLP.

natural language processing

NLP

trend survey

trend survey on Japanese natural language processing studies

trend surveys

Japanese natural language processing studies

Japanese NLP

natural language processing

trend survey on Japanese natural language processing studies

NLP

trend surveys


Input: High frequency oscillations -LRB- HFOs -RRB- are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution, intracranial EEG data. Two primary challenges are 1 -RRB- dimensionality reduction, and 2 -RRB- assessing feasibility of classification. Dimensionality reduction assumes that the data lie on a manifold with dimension less than that of the features space. However, previous HFO analysis have assumed a linear manifold, global across time, space -LRB- i.e. recording electrode/channel -RRB-, and individual patients. Instead, we assess both a -RRB- whether linear methods are appropriate and b -RRB- the consistency of the manifold across time, space, and patients. We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs -LRB- those occurring during seizures and those occurring due to other processes -RRB-. This analysis provides the foundation for future clinical use of HFO features and guides the analysis for other discrete events, such as individual action potentials or multi-unit activity.

High frequency oscillations -LRB- HFOs -RRB-

epileptic brain tissue and activity

HFOs

features space

linear manifold

manifold

bounds

Bayes classification error

HFOs

HFO features

discrete events

action potentials

multi-unit activity

analysis of discrete events

dimensionality reduction

assessing feasibility of classification

classification

Dimensionality reduction

HFO analysis

clinical use of HFO features

high-temporal resolution, intracranial EEG data

manifold

linear methods

those

those

high-temporal resolution, intracranial EEG data

analysis of discrete events

linear manifold

HFO analysis

those

HFOs

those

HFOs

action potentials

discrete events

multi-unit activity

discrete events

those

those

action potentials

multi-unit activity


Input: In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. We apply our system to the task of scoring alternative speech recognition hypotheses -LRB- SRH -RRB- in terms of their semantic coherence. We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent -LRB- given a baseline of 54.55% -RRB-.

ONTOSCORE

system

system

system

it

ontology

speech recognition hypotheses -LRB- SRH -RRB-

semantic coherence

speech recognition hypotheses

SRHs

German corpus

ontology

system

system

speech recognition hypotheses -LRB- SRH -RRB-


Input: We propose an efficient dialogue management for an information navigation system based on a document knowledge base. It is expected that incorporation of appropriate N-best candidates of ASR and contextual information will improve the system performance. The system also has several choices in generating responses or confirmations. In this paper, this selection is optimized as minimization of Bayes risk based on reward for correct information presentation and penalty for redundant turns. We have evaluated this strategy with our spoken dialogue system'' Dialogue Navigator for Kyoto City'', which also has question-answering capability. Effectiveness of the proposed framework was confirmed in the success rate of retrieval and the average number of turns for information access.

dialogue management

information navigation system

generating responses or confirmations

minimization of Bayes risk

spoken dialogue system'' Dialogue Navigator for Kyoto City''

document knowledge base

N-best candidates of ASR

contextual information

correct information presentation

redundant turns

question-answering capability

information access

system

system

strategy

framework

reward

penalty

success rate of retrieval

average number of turns

dialogue management

information navigation system

document knowledge base

information navigation system

N-best candidates of ASR

system

contextual information

system

system

generating responses or confirmations

reward

minimization of Bayes risk

reward

correct information presentation

penalty

minimization of Bayes risk

penalty

redundant turns

average number of turns

information access

N-best candidates of ASR

contextual information

reward

penalty

success rate of retrieval

average number of turns

spoken dialogue system'' Dialogue Navigator for Kyoto City''

strategy

success rate of retrieval

framework

average number of turns

framework

question-answering capability

spoken dialogue system'' Dialogue Navigator for Kyoto City''


Input: Reducing language model -LRB- LM -RRB- size is a critical issue when applying a LM to realistic applications which have memory constraints. In this paper, three measures are studied for the purpose of LM pruning. They are probability, rank, and entropy. We evaluated the performance of the three pruning criteria in a real application of Chinese text input in terms of character error rate -LRB- CER -RRB-. We first present an empirical comparison, showing that rank performs the best in most cases. We also show that the high-performance of rank lies in its strong correlation with error rate. We then present a novel method of combining two criteria in model pruning. Experimental results show that the combined criterion consistently leads to smaller models than the models pruned using either of the criteria separately, at the same CER.

Reducing language model -LRB- LM -RRB- size

LM pruning

model pruning

LM

memory constraints.

rank

entropy

pruning criteria

rank

rank

Chinese text input

character error rate -LRB- CER -RRB-

error rate

CER

method

rank

entropy

pruning criteria

Chinese text input

method

model pruning

character error rate -LRB- CER -RRB-

pruning criteria

error rate

rank


Input: This paper proposes an annotating scheme that encodes honorifics -LRB- respectful words -RRB-. Honorifics are used extensively in Japanese, reflecting the social relationship -LRB- e.g. social ranks and age -RRB- of the referents. This referential information is vital for resolving zero pronouns and improving machine translation outputs. Annotating honorifics is a complex task that involves identifying a predicate with honorifics, assigning ranks to referents of the predicate, calibrating the ranks, and connecting referents with their predicates.

annotating scheme

honorifics

Honorifics

Japanese

honorifics

honorifics

respectful words

referential information

zero pronouns

machine translation outputs

ranks

ranks

annotating scheme

honorifics

Honorifics

Japanese

referential information

zero pronouns

referential information

machine translation outputs

honorifics

respectful words