Input: This paper describes a system -LRB- RAREAS -RRB- which synthesizes marine weather forecasts directly from formatted weather data. Such synthesis appears feasible in certain natural sublanguages with stereotyped text structure. RAREAS draws on several kinds of linguistic and non-linguistic knowledge and mirrors a forecaster's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events. The approach can easily be adapted to synthesize bilingual or multi-lingual texts.

system

synthesis

approach

RAREAS

RAREAS

marine weather forecasts

formatted weather data

natural sublanguages with stereotyped text structure

bilingual or multi-lingual texts

linguistic and non-linguistic knowledge

system

marine weather forecasts

formatted weather data

system

natural sublanguages with stereotyped text structure

synthesis

linguistic and non-linguistic knowledge

RAREAS

bilingual or multi-lingual texts

approach


Input: When people use natural language in natural settings, they often use it ungrammatically, missing out or repeating words, breaking-off and restarting, speaking in fragments, etc.. Their human listeners are usually able to cope with these deviations with little difficulty. If a computer system wishes to accept natural language input from its users on a routine basis, it must display a similar indifference. In this paper, we outline a set of parsing flexibilities that such a system should provide. We go, on to describe FlexP, a bottom-up pattern-matching parser that we have designed and implemented to provide these flexibilities for restricted natural language input to a limited-domain computer system.

natural language

natural language input

parsing flexibilities

restricted natural language

it

system

flexibilities

computer system

FlexP

bottom-up pattern-matching parser

limited-domain computer system

FlexP

bottom-up pattern-matching parser

bottom-up pattern-matching parser

flexibilities

restricted natural language

bottom-up pattern-matching parser

flexibilities

restricted natural language

flexibilities

limited-domain computer system


Input: Oral communication is ubiquitous and carries important information yet it is also time consuming to document. Given the development of storage media and networks one could just record and store a conversation for documentation. The question is, however, how an interesting information piece would be found in a large database. Traditional information retrieval techniques use a histogram of keywords as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance. An alternative index could be the activity such as discussing, planning, informing, story-telling, etc.. This paper addresses the problem of the automatic detection of those activities in meeting situation and everyday rejoinders. Several extensions of this basic idea are being discussed and/or evaluated: Similar to activities one can define subsets of larger database and detect those automatically which is shown on a large database of TV shows. Emotions and other indices such as the dominance distribution of speakers might be available on the surface and could be used directly. Despite the small size of the databases used some results about the effectiveness of these indices can be obtained.

Oral communication

conversation

oral communication

database of TV shows

Emotions

storage media and networks

automatic detection

information retrieval techniques

histogram of keywords

document representation

activity

discussing

planning

informing

story-telling

dominance distribution of speakers

activities

activities

histogram of keywords

information retrieval techniques

histogram of keywords

document representation

activities

automatic detection

discussing

activity

planning

activity

informing

activity

story-telling

activity

discussing

planning

planning

informing

informing

story-telling


Input: Taiwan Child Language Corpus contains scripts transcribed from about 330 hours of recordings of fourteen young children from Southern Min Chinese speaking families in Taiwan. The format of the corpus adopts the Child Language Data Exchange System -LRB- CHILDES -RRB-. The size of the corpus is about 1.6 million words. In this paper, we describe data collection, transcription, word segmentation, and part-of-speech annotation of this corpus. Applications of the corpus are also discussed.

Taiwan Child Language Corpus

Child Language Data Exchange System -LRB- CHILDES -RRB-

corpus

corpus

corpus

corpus

data collection

transcription

word segmentation

part-of-speech annotation

Child Language Data Exchange System -LRB- CHILDES -RRB-

corpus

data collection

transcription

transcription

word segmentation

word segmentation

part-of-speech annotation

data collection

corpus

transcription

corpus

word segmentation

corpus

part-of-speech annotation

corpus


Input: This paper shows how dictionary word sense definitions can be analysed by applying a hierarchy of phrasal patterns. An experimental system embodying this mechanism has been implemented for processing definitions from the Longman Dictionary of Contemporary English. A property of this dictionary, exploited by the system, is that it uses a restricted vocabulary in its word sense definitions. The structures generated by the experimental system are intended to be used for the classification of new word senses in terms of the senses of words in the restricted vocabulary. Examples illustrating the output generated are presented, and some qualitative performance results and problems that were encountered are discussed. The analysis process applies successively more specific phrasal analysis rules as determined by a hierarchy of patterns in which less specific patterns dominate more specific ones. This ensures that reasonable incomplete analyses of the definitions are produced when more complete analyses are not possible, resulting in a relatively robust analysis mechanism. Thus the work reported addresses two robustness problems faced by current experimental natural language processing systems: coping with an incomplete lexicon and with incomplete knowledge of phrasal constructions.

dictionary word sense definitions

restricted vocabulary

word sense definitions

restricted vocabulary

phrasal analysis rules

incomplete knowledge of phrasal constructions

hierarchy of phrasal patterns

natural language processing systems

system

mechanism

dictionary

system

it

Longman Dictionary of Contemporary English

incomplete lexicon

classification of new word senses

robustness problems

hierarchy of phrasal patterns

dictionary word sense definitions

restricted vocabulary

it

restricted vocabulary

word sense definitions

restricted vocabulary

classification of new word senses

mechanism

system

robustness problems

natural language processing systems

incomplete lexicon

robustness problems

incomplete knowledge of phrasal constructions

robustness problems


Input: This paper presents a word segmentation system in France Telecom R&D Beijing, which uses a unified approach to word breaking and OOV identification. The output can be customized to meet different segmentation standards through the application of an ordered list of transformation. The system participated in all the tracks of the segmentation bakeoff -- PK-open, PK-closed, AS-open, AS-closed, HK-open, HK-closed, MSR-open and MSR-closed -- and achieved the state-of-the-art performance in MSR-open, MSR-close and PK-open tracks. Analysis of the results shows that each component of the system contributed to the scores.

word segmentation system

approach

system

system

word breaking

OOV identification

segmentation bakeoff

PK-open

PK-closed

AS-open

AS-closed

HK-open

HK-closed

MSR-open

MSR-closed

MSR-open

MSR-close

PK-open

approach

word segmentation system

approach

word breaking

approach

OOV identification

word breaking

OOV identification

PK-open

PK-closed

PK-closed

AS-open

AS-open

AS-closed

AS-closed

HK-open

HK-open

HK-closed

HK-closed

MSR-open

MSR-open

MSR-closed

MSR-open

MSR-close

MSR-close

PK-open

PK-open

segmentation bakeoff

PK-closed

segmentation bakeoff

AS-open

segmentation bakeoff

AS-closed

segmentation bakeoff

HK-open

segmentation bakeoff

HK-closed

segmentation bakeoff

MSR-open

segmentation bakeoff

MSR-closed

segmentation bakeoff


Input: This paper describes a method of interactively visualizing and directing the process of translating a sentence. The method allows a user to explore a model of syntax-based statistical machine translation -LRB- MT -RRB-, to understand the model's strengths and weaknesses, and to compare it to other MT systems. Using this visualization method, we can find and address conceptual and practical problems in an MT system. In our demonstration at ACL, new users of our tool will drive a syntax-based decoder for themselves.

method

method

model

model

it

interactively visualizing and directing the process of translating

syntax-based statistical machine translation -LRB- MT -RRB-

MT systems

visualization method

MT system

syntax-based decoder

method

interactively visualizing and directing the process of translating

method

model

model

syntax-based statistical machine translation -LRB- MT -RRB-

visualization method

MT system

it

MT systems


Input: A method of sense resolution is proposed that is based on WordNet, an on-line lexical database that incorporates semantic relations -LRB- synonymy, antonymy, hyponymy, meronymy, causal and troponymic entailment -RRB- as labeled pointers between word senses. With WordNet, it is easy to retrieve sets of semantically related words, a facility that will be used for sense resolution during text processing, as follows. When a word with multiple senses is encountered, one of two procedures will be followed. Either, -LRB- 1 -RRB- words related in meaning to the alternative senses of the polysemous word will be retrieved; new strings will be derived by substituting these related words into the context of the polysemous word; a large textual corpus will then be searched for these derived strings; and that sense will be chosen that corresponds to the derived string that is found most often in the corpus. Or, -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus; all words found to occur in that context will be noted; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful, this procedure could have practical applications to problems of information retrieval, mechanical translation, intelligent tutoring systems, and elsewhere.

method

procedures

procedure

sense resolution

sense resolution

text processing

information retrieval

mechanical translation

intelligent tutoring systems

WordNet

on-line lexical database

WordNet

WordNet

semantic relations

synonymy

antonymy

hyponymy

meronymy

causal and troponymic entailment

semantically related words

alternative senses

polysemous word

polysemous word

polysemous word

semantic distance

method

sense resolution

WordNet

method

WordNet

semantically related words

semantically related words

sense resolution

sense resolution

text processing

WordNet

semantic distance

procedure

information retrieval

procedure

mechanical translation

procedure

intelligent tutoring systems

WordNet

on-line lexical database

synonymy

semantic relations

antonymy

semantic relations

hyponymy

semantic relations

meronymy

semantic relations

causal and troponymic entailment

semantic relations

semantic relations

WordNet

synonymy

antonymy

antonymy

hyponymy

hyponymy

meronymy

meronymy

causal and troponymic entailment

information retrieval

mechanical translation

mechanical translation

intelligent tutoring systems


Input: The interlingual approach to MT has been repeatedly advocated by researchers originally interested in natural language understanding who take machine translation to be one possible application. However, not only the ambiguity but also the vagueness which every natural language inevitably has leads this approach into essential difficulties. In contrast, our project, the Mu-project, adopts the transfer approach as the basic framework of MT. This paper describes the detailed construction of the transfer phase of our system from Japanese to English, and gives some examples of problems which seem difficult to treat in the interlingual approach. The basic design principles of the transfer phase of our system have already been mentioned in -LRB- 1 -RRB- -LRB- 2 -RRB-. Some of the principles which are relevant to the topic of this paper are: -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- Form-Oriented Dictionary Description. This paper also shows how these principles are realized in the current system.

interlingual approach

Mu-project

transfer approach

interlingual approach

Multiple Layer of Grammars

Multiple Layer Presentation

Lexicon Driven Processing

Form-Oriented Dictionary Description

MT

transfer phase

system

transfer phase

system

principles

principles

system

natural language understanding

machine translation

MT

Japanese

English

interlingual approach

MT

Mu-project

MT

transfer approach

Mu-project

transfer phase

system

transfer phase

system

Multiple Layer of Grammars

principles

Multiple Layer Presentation

principles

Lexicon Driven Processing

principles

Form-Oriented Dictionary Description

principles

principles

system

Multiple Layer of Grammars

Multiple Layer Presentation

Multiple Layer Presentation

Lexicon Driven Processing

Lexicon Driven Processing

Form-Oriented Dictionary Description


Input: In this paper discourse segments are defined and a method for discourse segmentation primarily based on abduction of temporal relations between segments is proposed. This method is precise and computationally feasible and is supported by previous work in the area of temporal anaphora resolution.

discourse segments

method

method

discourse segmentation

abduction of temporal relations

temporal anaphora resolution

method

discourse segmentation

abduction of temporal relations

discourse segmentation

temporal anaphora resolution

method


Input: This paper describes to what extent deep processing may benefit from shallow techniques and it presents a NLP system which integrates a linguistic PoS tagger and chunker as a preprocessing module of a broad coverage unification based grammar of Spanish. Experiments show that the efficiency of the overall analysis improves significantly and that our system also provides robustness to the linguistic processing while maintaining both the accuracy and the precision of the grammar.

deep processing

linguistic processing

shallow techniques

NLP system

linguistic PoS tagger and chunker

broad coverage unification based grammar of Spanish

system

robustness

accuracy

precision

shallow techniques

deep processing

broad coverage unification based grammar of Spanish

NLP system

system

linguistic processing

linguistic PoS tagger and chunker

broad coverage unification based grammar of Spanish

robustness

system

accuracy

system

precision

system

accuracy

precision


Input: Joint image filters can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods rely on various kinds of explicit filter construction or hand-designed objective functions. It is thus difficult to understand, improve, and accelerate them in a coherent framework. In this paper, we propose a learning-based approach to construct a joint filter based on Convolution-al Neural Networks. In contrast to existing methods that consider only the guidance image, our method can selectively transfer salient structures that are consistent in both guidance and target images. We show that the model trained on a certain type of data, e.g., RGB and depth images, generalizes well for other modalities, e.g., Flash/Non-Flash and RGB/NIR images. We validate the effectiveness of the proposed joint filter through extensive comparisons with state-of-the-art methods.

Joint image filters

explicit filter construction

hand-designed objective functions

learning-based approach

joint filter

Convolution-al Neural Networks

joint filter

guidance image

guidance image

guidance image

RGB and depth images

Flash/Non-Flash and RGB/NIR images

suppressing noise

enhancing spatial resolution

transfer salient structures

Existing methods

them

coherent framework

methods

method

model

data

modalities

state-of-the-art methods

Joint image filters

suppressing noise

Joint image filters

enhancing spatial resolution

guidance image

Joint image filters

explicit filter construction

Existing methods

hand-designed objective functions

Existing methods

coherent framework

them

learning-based approach

joint filter

Convolution-al Neural Networks

learning-based approach

guidance image

methods

method

transfer salient structures

model

modalities

data

model

suppressing noise

enhancing spatial resolution

explicit filter construction

hand-designed objective functions

methods

method

joint filter

state-of-the-art methods

RGB and depth images

data

Flash/Non-Flash and RGB/NIR images

modalities


Input: In our current research into the design of cognitively well-motivated interfaces relying primarily on the display of graphical information, we have observed that graphical information alone does not provide sufficient support to users-particularly when situations arise that do not simply conform to the users' expectations. This can occur due to too much information being requested, too little, information of the wrong kind, etc.. To solve this problem, we are working towards the integration of natural language generation to augment the interaction

cognitively well-motivated interfaces

natural language generation

display of graphical information

graphical information

interaction

display of graphical information

cognitively well-motivated interfaces

natural language generation

interaction


Input: A central problem of word sense disambiguation -LRB- WSD -RRB- is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. Our investigation reveals that this method of acquiring sense-tagged data is promising. On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs.

word sense disambiguation -LRB- WSD -RRB-

acquiring sense-tagged data

evaluating WSD programs

WSD programs

manually sense-tagged data

sense-tagged training data

English-Chinese parallel corpora

SENSEVAL-2 English lexical sample task

manually sense-tagged data

supervised learning

approach

method

nouns

SENSEVAL-2 nouns

domain dependence

accuracy

sense coverage

manually sense-tagged data

supervised learning

approach

sense-tagged training data

approach

nouns

English-Chinese parallel corpora

sense-tagged training data

method

acquiring sense-tagged data

nouns

SENSEVAL-2 English lexical sample task

sense coverage

manually sense-tagged data

domain dependence

evaluating WSD programs


Input: This paper presents an analysis of temporal anaphora in sentences which contain quantification over events, within the framework of Discourse Representation Theory. The analysis in -LRB- Partee, 1984 -RRB- of quantified sentences, introduced by a temporal connective, gives the wrong truth-conditions when the temporal connective in the subordinate clause is before or after. This problem has been previously analyzed in -LRB- de Swart, 1991 -RRB- as an instance of the proportion problem and given a solution from a Generalized Quantifier approach. By using a careful distinction between the different notions of reference time based on -LRB- Kamp and Reyle, 1993 -RRB-, we propose a solution to this problem, within the framework of DRT. We show some applications of this solution to additional temporal anaphora phenomena in quantified sentences.

temporal anaphora

temporal anaphora phenomena

quantification over events

temporal connective

temporal connective

subordinate clause

proportion problem

reference time

Discourse Representation Theory

Generalized Quantifier approach

DRT

quantified sentences

quantified sentences

problem

solution

solution

problem

solution

quantification over events

temporal anaphora

temporal connective

subordinate clause

Discourse Representation Theory

temporal anaphora

Generalized Quantifier approach

problem

solution

problem

DRT

problem

solution

temporal anaphora phenomena

quantified sentences

solution


Input: In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training. Experimental results show that our method significantly outperforms state-of-the-art syntactic relation-based methods by up to 20% in MRR.

correlation of dependency relation paths

dependency relations

mapping score

answer extraction

correlation measure

correlation measure

MRR

approximate phrase mapping algorithm

Maximum Entropy-based ranking model

syntactic relation-based methods

correlations

method

correlation of dependency relation paths

answer extraction

correlation measure

dependency relations

mapping score

correlation measure

correlations

Maximum Entropy-based ranking model

method

syntactic relation-based methods

MRR

method

MRR

syntactic relation-based methods


Input: A meaningful evaluation methodology can advance the state-of-the-art by encouraging mature, practical applications rather than ``toy'' implementations. Evaluation is also crucial to assessing competing claims and identifying promising technical approaches. While work in speech recognition -LRB- SR -RRB- has a history of evaluation methodologies that permit comparison among various systems, until recently no methodology existed for either developers of natural language -LRB- NL -RRB- interfaces or researchers in speech understanding -LRB- SU -RRB- to evaluate and compare the systems they developed. Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems -LRB- SLS -RRB- program to agree on a methodology for comparative evaluation of SLS systems, and that methodology has been put into practice several times in comparative tests of several SLS systems. These evaluations are probably the only NL evaluations other than the series of Message Understanding Conferences -LRB- Sundheim, 1989; Sundheim, 1991 -RRB- to have been developed and used by a group of researchers at different sites, although several excellent workshops have been held to study some of these problems -LRB- Palmer et al., 1989; Neal et al., 1991 -RRB-. This paper describes a practical ``black-box'' methodology for automatic evaluation of question-answering NL systems. While each new application domain will require some development of special resources, the heart of the methodology is domain-independent, and it can be used with either speech or text input. The particular characteristics of the approach are described in the following section: subsequent sections present its implementation in the DARPA SLS community, and some problems and directions for future development.

evaluation methodology

Evaluation

speech recognition -LRB- SR -RRB-

speech understanding -LRB- SU -RRB-

evaluation of SLS systems

NL evaluations

automatic evaluation of question-answering NL systems

approaches

evaluation methodologies

methodology

methodology

evaluations

methodology

it

approach

natural language -LRB- NL -RRB- interfaces

speech or text input

SLS systems

SLS systems

Message Understanding Conferences

``black-box'' methodology

Evaluation

approaches

speech or text input

it

methodology

evaluation of SLS systems

methodology

SLS systems

``black-box'' methodology

automatic evaluation of question-answering NL systems

evaluations

NL evaluations

evaluations

Message Understanding Conferences


Input: In this paper we present a novel autonomous pipeline to build a personalized parametric model -LRB- pose-driven avatar -RRB- using a single depth sensor. Our method first captures a few high-quality scans of the user rotating herself at multiple poses from different views. We fit each incomplete scan using template fitting techniques with a generic human template, and register all scans to every pose using global consistency constraints. After registration, these watertight models with different poses are used to train a parametric model in a fashion similar to the SCAPE method. Once the parametric model is built, it can be used as an anim-itable avatar or more interestingly synthesizing dynamic 3D models from single-view depth videos. Experimental results demonstrate the effectiveness of our system to produce dynamic models.

autonomous pipeline

method

it

system

personalized parametric model -LRB- pose-driven avatar -RRB-

template fitting techniques

watertight models

parametric model

SCAPE method

parametric model

dynamic 3D models

dynamic models

single depth sensor

human template

global consistency constraints

anim-itable avatar

single-view depth videos

autonomous pipeline

personalized parametric model -LRB- pose-driven avatar -RRB-

single depth sensor

autonomous pipeline

human template

template fitting techniques

watertight models

parametric model

SCAPE method

parametric model

it

anim-itable avatar

it

dynamic 3D models

single-view depth videos

dynamic 3D models

system

dynamic models


Input: In this paper, we propose a novel algorithm to detect/compensate on-line interference effects when integrating Global Navigation Satellite System -LRB- GNSS -RRB- and Inertial Navigation System -LRB- INS -RRB-. The GNSS/INS coupling is usually performed by an Extended Kalman Filter -LRB- EKF -RRB- which yields an accurate and robust localization. However, interference cause the GNSS measurement noise to increase unexpectedly, hence degrade the positioning accuracy. In this context, our contribution is twofold. We first study the impact of the GNSS noise inflation on the covariance of the EKF outputs so as to compute a least square estimate of the potential variance jumps. Then, this estimation is used in a Bayesian test which decides whether interference are corrupting the GNSS signal or not. It allows us to estimate their times of occurrence as well. In this way, the impaired measurements can be discarded while their impact on the navigation solution can be compensated. The results show the performance of the proposed approach on simulated data.

algorithm

estimation

approach

on-line interference effects

Global Navigation Satellite System -LRB- GNSS -RRB-

Inertial Navigation System -LRB- INS -RRB-

GNSS/INS coupling

accurate and robust localization

navigation solution

Extended Kalman Filter -LRB- EKF -RRB-

least square estimate

Bayesian test

interference

GNSS measurement noise

covariance

EKF outputs

variance jumps

GNSS signal

positioning accuracy

simulated data

algorithm

on-line interference effects

Extended Kalman Filter -LRB- EKF -RRB-

GNSS/INS coupling

Extended Kalman Filter -LRB- EKF -RRB-

accurate and robust localization

least square estimate

variance jumps

estimation

Bayesian test

Global Navigation Satellite System -LRB- GNSS -RRB-

Inertial Navigation System -LRB- INS -RRB-

EKF outputs

covariance

simulated data

approach


Input: We propose a unified variational formulation for joint motion estimation and segmentation with explicit occlusion handling. This is done by a multi-label representation of the flow field, where each label corresponds to a parametric representation of the motion. We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints. Explicit occlusion handling eliminates errors otherwise created by the regularization. As occlusions can occur only at object boundaries, a large number of objects may be required. By using a fast primal-dual algorithm we are able to handle several hundred motion segments. Results are shown on several classical motion segmentation and optical flow examples.

unified variational formulation

explicit occlusion handling

multi-label representation of the flow field

convex formulation

multi-label Potts model

Explicit occlusion handling

primal-dual algorithm

joint motion estimation and segmentation

parametric representation of the motion

classical motion segmentation

optical flow

asymmetric map-uniqueness criterion

formulation

convex constraints

regularization

occlusions

object boundaries

motion segments

unified variational formulation

joint motion estimation and segmentation

explicit occlusion handling

unified variational formulation

convex formulation

multi-label Potts model

convex constraints

formulation

primal-dual algorithm

motion segments

asymmetric map-uniqueness criterion

formulation


Input: Two main classes of approaches have been studied to perform monocular nonrigid 3D reconstruction: Template-based methods and Non-rigid Structure from Motion techniques. While the first ones have been applied to reconstruct poorly-textured surfaces, they assume the availability of a 3D shape model prior to reconstruction. By contrast, the second ones do not require such a shape template, but, instead, rely on points being tracked throughout a video sequence, and are thus ill-suited to handle poorly-textured surfaces. In this paper, we introduce a template-free approach to reconstructing a poorly-textured, deformable surface. To this end, we leverage surface isometry and formulate 3D reconstruction as the joint problem of non-rigid image registration and depth estimation. Our experiments demonstrate that our approach yields much more accurate 3D reconstructions than state-of-the-art techniques.

approaches

ones

they

ones

approach

state-of-the-art techniques

monocular nonrigid 3D reconstruction

reconstruction

3D reconstruction

joint problem of non-rigid image registration and depth estimation

3D reconstructions

Template-based methods

Non-rigid Structure from Motion techniques

3D shape model

template-free approach

surface isometry

poorly-textured surfaces

shape template

poorly-textured surfaces

poorly-textured, deformable surface

video sequence

approaches

monocular nonrigid 3D reconstruction

ones

poorly-textured surfaces

3D shape model

they

template-free approach

poorly-textured, deformable surface

surface isometry

3D reconstruction

joint problem of non-rigid image registration and depth estimation

3D reconstruction

Template-based methods

approaches

Non-rigid Structure from Motion techniques

approaches

Template-based methods

Non-rigid Structure from Motion techniques

approach

state-of-the-art techniques

3D reconstructions

approach

3D reconstructions

state-of-the-art techniques


Input: Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regu-larization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and Me-diaMill demonstrate the effectiveness and efficiency of the proposed algorithm.

computer vision applications

image classification

video indexing

multi-label classification problems

multi-label classification problems

optimization problem

multi-label classification approach

SVM like learning system

Rank-HLapSVM

dual coordinate descent method

hypergraph regu-larization

hypergraph

hypergraph regularization

real datasets

algorithm

ImageCLEF

Me-diaMill

image classification

computer vision applications

video indexing

computer vision applications

Rank-HLapSVM

SVM like learning system

ImageCLEF

real datasets

Me-diaMill

real datasets

image classification

video indexing

ImageCLEF

Me-diaMill

multi-label classification problems

computer vision applications

SVM like learning system

multi-label classification problems

dual coordinate descent method

optimization problem

hypergraph regu-larization

multi-label classification approach

hypergraph regularization

SVM like learning system

real datasets

algorithm


Input: We derive a convex optimization problem for the task of segmenting sequential data, which explicitly treats presence of outliers. We describe two algorithms for solving this problem, one exact and one a top-down novel approach, and we derive a consistency results for the case of two segments and no outliers. Robustness to outliers is evaluated on two real-world tasks related to speech segmentation. Our algorithms outperform baseline seg-mentation algorithms.

convex optimization problem

segmenting sequential data

real-world tasks

speech segmentation

outliers

outliers

algorithms

problem

algorithms

Robustness

baseline seg-mentation algorithms

convex optimization problem

segmenting sequential data

segmenting sequential data

outliers

algorithms

problem

outliers

Robustness

speech segmentation

real-world tasks

real-world tasks

Robustness

speech segmentation

Robustness

algorithms

baseline seg-mentation algorithms


Input: This paper examines the properties of feature-based partial descriptions built on top of Halliday's systemic networks. We show that the crucial operation of consistency checking for such descriptions is NP-complete, and therefore probably intractable, but proceed to develop algorithms which can sometimes alleviate the unpleasant consequences of this intractability.

feature-based partial descriptions

Halliday's systemic networks

consistency checking

descriptions

Halliday's systemic networks

feature-based partial descriptions

consistency checking

descriptions


Input: We describe Yoopick, a combinatorial sports prediction market that implements a flexible betting language, and in turn facilitates fine-grained probabilistic estimation of outcomes.

Yoopick

combinatorial sports prediction market

flexible betting language

fine-grained probabilistic estimation of outcomes

Yoopick

combinatorial sports prediction market

Yoopick

fine-grained probabilistic estimation of outcomes

flexible betting language

Yoopick


Input: The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1 -RRB- to be representative, they need to occur frequently enough in the visual world; 2 -RRB- to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects,'' visual phrases'', etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discrim-inative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset.

discriminative patches

iterative procedure

clustering

training discriminative classifiers

cross-validation

discriminative patches

discrim-inative patches

unsupervised mid-level visual representation

unsupervised discriminative clustering problem

unsupervised mid-level visual representation

supervised regime

scene classification

patches

this

it

they

image patches

MIT Indoor-67 dataset

overfitting

discriminative patches

unsupervised mid-level visual representation

image patches

unsupervised discriminative clustering problem

cross-validation

overfitting

discriminative patches

unsupervised mid-level visual representation

it

discriminative patches

discrim-inative patches

supervised regime

scene classification

supervised regime

MIT Indoor-67 dataset

they


Input: We investigate the utility of an algorithm for translation lexicon acquisition -LRB- SABLE -RRB-, used previously on a very large corpus to acquire general translation lexicons, when that algorithm is applied to a much smaller corpus to produce candidates for domain-specific translation lexicons.

algorithm

algorithm

translation lexicon acquisition -LRB- SABLE -RRB-

general translation lexicons

domain-specific translation lexicons

algorithm

translation lexicon acquisition -LRB- SABLE -RRB-

algorithm

general translation lexicons

algorithm

domain-specific translation lexicons


Input: This paper describes a computational model of word segmentation and presents simulation results on realistic acquisition. In particular, we explore the capacity and limitations of statistical learning mechanisms that have recently gained prominence in cognitive psychology and linguistics.

computational model

statistical learning mechanisms

word segmentation

realistic acquisition

cognitive psychology

linguistics

computational model

word segmentation

computational model

realistic acquisition

cognitive psychology

statistical learning mechanisms

linguistics

statistical learning mechanisms

cognitive psychology

linguistics


Input: In the model-based policy search approach to reinforcement learning -LRB- RL -RRB-, policies are found using a model -LRB- or ``simulator'' -RRB- of the Markov decision process. However, for high-dimensional continuous-state tasks, it can be extremely difficult to build an accurate model, and thus often the algorithm returns a policy that works in simulation but not in real-life. The other extreme, model-free RL, tends to require infeasibly large numbers of real-life trials. In this paper, we present a hybrid algorithm that requires only an approximate model, and only a small number of real-life trials. The key idea is to successively ``ground'' the policy evaluations using real-life trials, but to rely on the approximate model to suggest local changes. Our theoretical results show that this algorithm achieves near-optimal performance in the real system, even when the model is only approximate. Empirical results also demonstrate that -- when given only a crude model and a small number of real-life trials -- our algorithm can obtain near-optimal performance in the real system.

model-based policy search approach

hybrid algorithm

approximate model

approximate model

reinforcement learning -LRB- RL -RRB-

high-dimensional continuous-state tasks

model-free RL

policy evaluations

policies

Markov decision process

policy

real-life trials

real-life trials

real-life trials

near-optimal performance

real-life trials

near-optimal performance

model

algorithm

algorithm

crude model

algorithm

model-based policy search approach

reinforcement learning -LRB- RL -RRB-

Markov decision process

policies

model

high-dimensional continuous-state tasks

algorithm

policy

real-life trials

model-free RL

approximate model

hybrid algorithm

real-life trials

approximate model

real-life trials

policy evaluations

crude model

algorithm

real-life trials

algorithm

crude model

real-life trials


Input: Although every natural language system needs a computational lexicon, each system puts different amounts and types of information into its lexicon according to its individual needs. However, some of the information needed across systems is shared or identical information. This paper presents our experience in planning and building COMPLEX, a computational lexicon designed to be a repository of shared lexical information for use by Natural Language Processing -LRB- NLP -RRB- systems. We have drawn primarily on explicit and implicit information from machine-readable dictionaries -LRB- MRD's -RRB- to create a broad coverage lexicon.

natural language system

COMPLEX

computational lexicon

computational lexicon

shared lexical information

system

Natural Language Processing -LRB- NLP -RRB- systems

machine-readable dictionaries -LRB- MRD's -RRB-

computational lexicon

natural language system

COMPLEX

Natural Language Processing -LRB- NLP -RRB- systems

COMPLEX

computational lexicon


Input: Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan-generator -LRB- SPG -RRB- generates a potentially large list of possible sentence plans for a given text-plan input. Second, the sentence-plan-ranker -LRB- SPR -RRB- ranks the list of output sentence plans, and then selects the top-ranked plan. The SPR uses ranking rules automatically learned from training data. We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.

Sentence planning

sentence scoping

tasks

methodology

syntactic structure

speech acts

sentence plans

text-plan input

sentence plans

ranking rules

sentence plan

top human-ranked sentence plan

SPoT

sentence planner

SPoT

randomized sentence-plan-generator -LRB- SPG -RRB-

sentence-plan-ranker -LRB- SPR -RRB-

SPR

SPR

sentence scoping

tasks

syntactic structure

speech acts

methodology

SPoT

randomized sentence-plan-generator -LRB- SPG -RRB-

sentence plans

text-plan input

randomized sentence-plan-generator -LRB- SPG -RRB-

sentence-plan-ranker -LRB- SPR -RRB-

sentence plans

ranking rules

SPR

SPR

sentence plan

SPoT

sentence planner

sentence plan

top human-ranked sentence plan


Input: We discuss maximum a posteriori estimation of continuous density hidden Markov models -LRB- CDHMM -RRB-. The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training. New experimental results on all four applications are provided to show the effectiveness of the MAP estimation approach.

maximum a posteriori estimation

continuous density hidden Markov models -LRB- CDHMM -RRB-

MLE reestimation algorithms

forward-backward algorithm

segmental k-means algorithm

HMM with Gaussian mixture observation densities

Bayesian learning

MAP estimation approach

reestimation formulas

speech recognition applications

parameter smoothing

speaker adaptation

speaker group modeling

corrective training

applications

maximum a posteriori estimation

continuous density hidden Markov models -LRB- CDHMM -RRB-

reestimation formulas

HMM with Gaussian mixture observation densities

Bayesian learning

speech recognition applications

forward-backward algorithm

MLE reestimation algorithms

segmental k-means algorithm

MLE reestimation algorithms

parameter smoothing

speech recognition applications

speaker adaptation

speech recognition applications

speaker group modeling

speech recognition applications

corrective training

speech recognition applications

segmental k-means algorithm

forward-backward algorithm

parameter smoothing

speaker adaptation

speaker adaptation

speaker group modeling

speaker group modeling

corrective training

applications

MAP estimation approach


Input: This paper describes a characters-based Chinese collocation system and discusses the advantages of it over a traditional word-based system. Since wordbreaks are not conventionally marked in Chinese text corpora, a character-based collocation system has the dual advantages of avoiding pre-processing distortion and directly accessing sub-lexical information. Furthermore, word-based collocational properties can be obtained through an auxiliary module of automatic segmentation.

characters-based Chinese collocation system

word-based system

character-based collocation system

auxiliary module of automatic segmentation

it

Chinese text corpora

avoiding pre-processing distortion

accessing sub-lexical information

word-based collocational properties

it

word-based system

avoiding pre-processing distortion

character-based collocation system

accessing sub-lexical information

character-based collocation system

avoiding pre-processing distortion

accessing sub-lexical information

auxiliary module of automatic segmentation

word-based collocational properties


Input: This paper describes a method for utterance classification that does not require manual transcription of training data. The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. The classification accuracy of the method is evaluated on three different spoken language system domains.

method

method

method

domain

model

method

utterance classification

utterance classification

manual transcription

manual transcription

spoken language system domains

domain independent acoustic models

classifiers

word-trigram recognition

unsupervised training

phone n-gram model

phone-string classifier

classification accuracy

method

utterance classification

method

utterance classification

word-trigram recognition

method

manual transcription

word-trigram recognition

unsupervised training

phone n-gram model

unsupervised training

domain

domain independent acoustic models

method

classifiers

method

unsupervised training

method

classifiers

domain independent acoustic models

classification accuracy

method

spoken language system domains

method


Input: The Interval Algebra -LRB- IA -RRB- and a subset of the Region Connection Calculus -LRB- RCC -RRB-, namely RCC-8, are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively. Such qualitative information can be formulated as a Qualitative Constraint Network -LRB- QCN -RRB-. In this paper, we focus on the minimal labeling problem -LRB- MLP -RRB- and we propose an algorithm to efficiently derive all the feasible base relations of a QCN. Our algorithm considers chordal QCNs and a new form of partial consistency which we define as ◆ G-consistency. Further, the proposed algorithm uses tractable subclasses of relations having a specific patchwork property for which-consistency implies the consistency of the input QCN. Experi-mentations with QCNs of IA and RCC-8 show the importance and efficiency of this new approach.

Interval Algebra -LRB- IA -RRB-

Region Connection Calculus -LRB- RCC -RRB-

Artificial Intelligence approaches

Qualitative Constraint Network -LRB- QCN -RRB-

QCN

chordal QCNs

QCN

QCNs

QCNs of IA and RCC-8

IA

representing and reasoning about qualitative temporal and topological relations

minimal labeling problem -LRB- MLP -RRB-

qualitative temporal and topological relations

qualitative information

partial consistency

◆ G-consistency

patchwork property

algorithm

algorithm

algorithm

approach

Interval Algebra -LRB- IA -RRB-

Region Connection Calculus -LRB- RCC -RRB-

chordal QCNs

partial consistency

Interval Algebra -LRB- IA -RRB-

Artificial Intelligence approaches

Region Connection Calculus -LRB- RCC -RRB-

Artificial Intelligence approaches

Region Connection Calculus -LRB- RCC -RRB-

◆ G-consistency

partial consistency

Artificial Intelligence approaches

representing and reasoning about qualitative temporal and topological relations

Qualitative Constraint Network -LRB- QCN -RRB-

qualitative information

algorithm

QCN

chordal QCNs

algorithm

partial consistency

algorithm

QCNs of IA and RCC-8

approach


Input: In this paper a morphological component with a limited capability to automatically interpret -LRB- and generate -RRB- derived words is presented. The system combines an extended two-level morphology -LSB- Trost, 1991a; Trost, 1991b -RSB- with a feature-based word grammar building on a hierarchical lexicon. Polymorphemic stems not explicitly stored in the lexicon are given a compositional interpretation. That way the system allows to minimize redundancy in the lexicon because derived words that are transparent need not to be stored explicitly. Also, words formed ad-hoc can be recognized correctly. The system is implemented in CommonLisp and has been tested on examples from German derivation.

morphological component

feature-based word grammar

CommonLisp

derived words

two-level morphology

hierarchical lexicon

Polymorphemic stems

compositional interpretation

derived words

words formed ad-hoc

system

system

system

German derivation

morphological component

derived words

two-level morphology

system

hierarchical lexicon

feature-based word grammar

CommonLisp

system

two-level morphology

feature-based word grammar

compositional interpretation

Polymorphemic stems

German derivation

system


Input: Lyric-based song sentiment classification seeks to assign songs appropriate sentiment labels such as light-hearted heavy-hearted. Four problems render vector space model -LRB- VSM -RRB--based text classification approach ineffective: 1 -RRB- Many words within song lyrics actually contribute little to sentiment; 2 -RRB- Nouns and verbs used to express sentiment are ambiguous; 3 -RRB- Negations and modifiers around the sentiment keywords make particular contributions to sentiment; 4 -RRB- Song lyric is usually very short. To address these problems, the sentiment vector space model -LRB- s-VSM -RRB- is proposed to represent song lyric document. The preliminary experiments prove that the s-VSM model outperforms the VSM model in the lyric-based song sentiment classification task.

Lyric-based song sentiment classification

lyric-based song sentiment classification task

vector space model -LRB- VSM -RRB--based text classification approach

sentiment vector space model -LRB- s-VSM -RRB-

s-VSM model

VSM model

song lyrics

Song lyric

song lyric document

sentiment

Negations

modifiers

sentiment keywords

sentiment

Negations

modifiers

Negations

sentiment

modifiers

sentiment

sentiment vector space model -LRB- s-VSM -RRB-

song lyric document

s-VSM model

VSM model

lyric-based song sentiment classification task

s-VSM model

lyric-based song sentiment classification task

VSM model


Input: We present an efficient algorithm for the redundancy elimination problem: Given an underspecified semantic representation -LRB- USR -RRB- of a scope ambiguity, compute an USR with fewer mutually equivalent readings. The algorithm operates on underspecified chart representations which are derived from dominance graphs; it can be applied to the USRs computed by large-scale grammars. We evaluate the algorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime.

algorithm

algorithm

it

algorithm

it

redundancy elimination problem

underspecified semantic representation -LRB- USR -RRB-

USR

underspecified chart representations

USRs

large-scale grammars

scope ambiguity

dominance graphs

degree of ambiguity

equivalent readings

algorithm

redundancy elimination problem

underspecified semantic representation -LRB- USR -RRB-

scope ambiguity

equivalent readings

USR

algorithm

underspecified chart representations

dominance graphs

underspecified chart representations

it

USRs

large-scale grammars

USRs

it

degree of ambiguity


Input: Currently several grammatical formalisms converge towards being declarative and towards utilizing context-free phrase-structure grammar as a backbone, e.g. LFG and PATR-II. Typically the processing of these formalisms is organized within a chart-parsing framework. The declarative character of the formalisms makes it important to decide upon an overall optimal control strategy on the part of the processor. In particular, this brings the rule-invocation strategy into critical focus: to gain maximal processing efficiency, one has to determine the best way of putting the rules to use. The aim of this paper is to provide a survey and a practical comparison of fundamental rule-invocation strategies within context-free chart parsing.

grammatical formalisms

context-free phrase-structure grammar

chart-parsing framework

optimal control strategy

rule-invocation strategy

rule-invocation strategies

context-free chart parsing

LFG

PATR-II

rules

formalisms

formalisms

context-free phrase-structure grammar

grammatical formalisms

LFG

grammatical formalisms

PATR-II

grammatical formalisms

LFG

PATR-II

chart-parsing framework

formalisms

rule-invocation strategies

context-free chart parsing


Input: Terminology structuring has been the subject of much work in the context of terms extracted from corpora: given a set of terms, obtained from an existing resource or extracted from a corpus, identifying hierarchical -LRB- or other types of -RRB- relations between these terms. The present paper focusses on terminology structuring by lexical methods, which match terms on the basis on their content words, taking morphological variants into account. Experiments are done on a `flat' list of terms obtained from an originally hierarchically-structured terminology: the French version of the US National Library of Medicine MeSH thesaurus. We compare the lexically-induced relations with the original MeSH relations: after a quantitative evaluation of their congruence through recall and precision metrics, we perform a qualitative, human analysis ofthe `new' relations not present in the MeSH. This analysis shows, on the one hand, the limits of the lexical structuring method. On the other hand, it also reveals some specific structuring choices and naming conventions made by the MeSH designers, and emphasizes ontological commitments that can not be left to automatic structuring.

Terminology structuring

terminology structuring

automatic structuring

corpus

hierarchically-structured terminology

US National Library of Medicine MeSH thesaurus

hierarchical -LRB- or other types of -RRB- relations

morphological variants

lexically-induced relations

MeSH relations

MeSH

MeSH

lexical methods

lexical structuring method

recall and precision metrics

lexical methods

terminology structuring

US National Library of Medicine MeSH thesaurus

hierarchically-structured terminology

lexically-induced relations

MeSH relations

recall and precision metrics

MeSH relations


Input: In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. We conducted experiments with an EBMT system. The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.

EBMT

language model

EBMT system

language model

small-sized bilingual corpus

out-of-domain bilingual corpus

in-domain monolingual corpus

out-of-domain bilingual corpus

evaluation measures

BLEU score

NIST score

small-sized bilingual corpus

EBMT

out-of-domain bilingual corpus

EBMT

language model

EBMT

in-domain monolingual corpus

language model

evaluation measures

out-of-domain bilingual corpus

evaluation measures

language model

BLEU score

evaluation measures

NIST score

evaluation measures

BLEU score

NIST score


Input: Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs -LRB- DPG -RRB- as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs.

Diagrams

Diagram Parse Graphs -LRB- DPG -RRB-

DPGs

LSTM-based method

DPG-based attention model

DPGs

complex concepts

relationships

events

structure of a diagram

structure of diagrams

natural images

natural images

diagrams

Understanding natural images

computer vision

diagram understanding

diagram interpretation and reasoning

syntactic parsing of diagrams

semantic interpretation and reasoning of diagrams

diagram question answering

syntactic parsing of diagrams

diagram question answering

syntactic parsing and question answering in diagrams

task

dataset

models

Diagrams

complex concepts

Diagrams

relationships

Diagrams

events

task

structure of a diagram

Diagram Parse Graphs -LRB- DPG -RRB-

structure of diagrams

syntactic parsing of diagrams

DPGs

semantic interpretation and reasoning of diagrams

diagram question answering

LSTM-based method

syntactic parsing of diagrams

DPG-based attention model

diagram question answering

models

syntactic parsing and question answering in diagrams

DPGs

models

complex concepts

relationships

relationships

events

Understanding natural images

computer vision

Understanding natural images

diagram understanding

diagrams

dataset


Input: Detecting fine-grained subtle changes among a scene is critically important in practice. Previous change detection methods, focusing on detecting large-scale significant changes, can not do this well. This paper proposes a feasible end-to-end approach to this challenging problem. We start from active camera relocation that quickly relocates camera to nearly the same pose and position of the last time observation. To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions. Given two times observations, we formulate fine-grained change detection as a joint optimization problem of three related factors, i.e., normal-aware lighting difference, camera geometry correction flow, and real scene change mask. We solve the three factors in a coarse-to-fine manner and achieve reliable change decision by rank minimization. We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions. Extensive experiments show the superior performance of our approach over state-of-the-art change detection methods and its ability to distinguish real scene changes from false ones caused by lighting variations.

Detecting fine-grained subtle changes among a scene

detecting large-scale significant changes

fine-grained change detection

joint optimization problem

fine-grained change detection of misaligned scenes

change detection methods

end-to-end approach

active camera relocation

rank minimization

change detection methods

problem

factors

factors

approach

detection sensitivity

accuracy

illuminations

normal-aware lighting difference

camera geometry correction flow

real scene change mask

coarse-to-fine manner

change decision

varied multiple lighting conditions

real scene changes

lighting variations

real-world datasets

change detection methods

detecting large-scale significant changes

end-to-end approach

problem

joint optimization problem

fine-grained change detection

coarse-to-fine manner

factors

rank minimization

change decision

approach

real scene changes

factors

joint optimization problem

varied multiple lighting conditions

fine-grained change detection of misaligned scenes

normal-aware lighting difference

factors

camera geometry correction flow

factors

real scene change mask

factors

normal-aware lighting difference

camera geometry correction flow

camera geometry correction flow

real scene change mask

real-world datasets

fine-grained change detection of misaligned scenes

approach

change detection methods


Input: Automatic evaluation metrics for Machine Translation -LRB- MT -RRB- systems, such as BLEU or NIST, are now well established. Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of the word segmentation problem. This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level. The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs.

Automatic evaluation metrics

BLEU

NIST

BLEU

BLEU

Machine Translation -LRB- MT -RRB- systems

assessment of language pairs

word segmentation problem

word segmentation problem

they

it

language pairs

English-Chinese

English-Japanese

character level

character level

word n-grams

commercial systems

statistical MT systems

Automatic evaluation metrics

Machine Translation -LRB- MT -RRB- systems

it

commercial systems

it

statistical MT systems

BLEU

Automatic evaluation metrics

NIST

Automatic evaluation metrics

English-Chinese

language pairs

English-Japanese

language pairs

BLEU

NIST

English-Chinese

English-Japanese

word n-grams

character level

they

assessment of language pairs

BLEU

word n-grams

BLEU

character level

BLEU

character level

BLEU

word segmentation problem

commercial systems

statistical MT systems


Input: This paper proposes a series of modifications to the left corner parsing algorithm for context-free grammars. It is argued that the resulting algorithm is both efficient and flexible and is, therefore, a good choice for the parser used in a natural language interface.

left corner parsing algorithm

context-free grammars

parser

algorithm

natural language interface

left corner parsing algorithm

context-free grammars

algorithm

parser

parser

natural language interface


Input: This paper presents a novel statistical singing voice conversion -LRB- SVC -RRB- technique with direct waveform modification based on the spectrum differential that can convert voice timbre of a source singer into that of a target singer without using a vocoder to generate converted singing voice waveforms. SVC makes it possible to convert singing voice characteristics of an arbitrary source singer into those of an arbitrary target singer. However, speech quality of the converted singing voice is significantly degraded compared to that of a natural singing voice due to various factors, such as analysis and modeling errors in the vocoder-based framework. To alleviate this degradation, we propose a statistical conversion process that directly modifies the signal in the waveform domain by estimating the difference in the spectra of the source and target singers' singing voices. The differential spectral feature is directly estimated using a differential Gaussian mixture model -LRB- GMM -RRB- that is analytically derived from the traditional GMM used as a conversion model in the conventional SVC. The experimental results demonstrate that the proposed method makes it possible to significantly improve speech quality in the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional SVC.

statistical singing voice conversion -LRB- SVC -RRB- technique

direct waveform modification

vocoder

SVC

vocoder-based framework

statistical conversion process

differential Gaussian mixture model -LRB- GMM -RRB-

GMM

conversion model

SVC

SVC

spectrum differential

voice timbre

converted singing voice waveforms

singing voice characteristics

converted singing voice

natural singing voice

spectra

differential spectral feature

speech quality

speech quality

conversion accuracy of singer identity

method

direct waveform modification

statistical singing voice conversion -LRB- SVC -RRB- technique

spectrum differential

direct waveform modification

spectrum differential

voice timbre

vocoder

converted singing voice waveforms

SVC

singing voice characteristics

differential Gaussian mixture model -LRB- GMM -RRB-

differential spectral feature

GMM

differential Gaussian mixture model -LRB- GMM -RRB-

GMM

conversion model

conversion model

SVC

conversion accuracy of singer identity

SVC

speech quality

converted singing voice

speech quality

natural singing voice

speech quality

method

speech quality

SVC

conversion accuracy of singer identity

method

converted singing voice

natural singing voice

method

SVC


Input: In this paper, we state the challenges of high-level program execution in multi-agent settings. We first introduce high-level program execution and the related work. Then we describe the completed work, the future work and its approaches. We conclude with the expected contributions of our research.

high-level program execution

high-level program execution


Input: During late-2013 through early-2014 NIST coordinated a special i-vector challenge based on data used in previous NIST Speaker Recognition Evaluations -LRB- SREs -RRB-. Unlike evaluations in the SRE series, the i-vector challenge was run entirely online and used fixed-length feature vectors projected into a low-dimensional space -LRB- i-vectors -RRB- rather than audio recordings. These changes made the challenge more readily accessible, especially to participants from outside the audio processing field. Compared to the 2012 SRE, the i-vector challenge saw an increase in the number of participants by nearly a factor of two, and a two orders of magnitude increase in the number of systems submitted for evaluation. Initial results indicate the leading system achieved an approximate 37% improvement relative to the baseline system.

i-vector challenge

NIST Speaker Recognition Evaluations -LRB- SREs -RRB-

SRE series

i-vector challenge

audio recordings

SRE

i-vector challenge

fixed-length feature vectors

low-dimensional space -LRB- i-vectors -RRB-

audio processing field

leading system

baseline system

NIST Speaker Recognition Evaluations -LRB- SREs -RRB-

i-vector challenge

fixed-length feature vectors

low-dimensional space -LRB- i-vectors -RRB-

low-dimensional space -LRB- i-vectors -RRB-

i-vector challenge

audio recordings

low-dimensional space -LRB- i-vectors -RRB-

SRE

i-vector challenge

leading system

baseline system


Input: Theoretical research in the area of machine translation usually involves the search for and creation of an appropriate formalism. An important issue in this respect is the way in which the compositionality of translation is to be defined. In this paper, we will introduce the anaphoric component of the Mimo formalism. It makes the definition and translation of anaphoric relations possible, relations which are usually problematic for systems that adhere to strict compositionality. In Mimo, the translation of anaphoric relations is compositional. The anaphoric component is used to define linguistic phenomena such as wh-movement, the passive and the binding of reflexives and pronouns mono-lingually. The actual working of the component will be shown in this paper by means of a detailed discussion of wh-movement.

machine translation

translation

translation of anaphoric relations

translation of anaphoric relations

formalism

anaphoric component

Mimo formalism

anaphoric component

strict compositionality

Mimo

linguistic phenomena

wh-movement

the passive and the binding of reflexives and pronouns

wh-movement

formalism

machine translation

Mimo

translation of anaphoric relations

anaphoric component

linguistic phenomena

anaphoric component

Mimo formalism

wh-movement

linguistic phenomena

the passive and the binding of reflexives and pronouns

linguistic phenomena

wh-movement

the passive and the binding of reflexives and pronouns


Input: A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD's. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.

recognition scheme

indexing descriptors

local region descriptors

vocabulary tree approach

efficiency

quality

retrieval quality

recognition quality

CD-covers

images of popular music CD's

database with ground truth

database

scheme

local regions

background clutter

occlusion

vocabulary tree

vocabulary tree

tree

quantization

quantization

indexing

retrieval

efficiency

quality

background clutter

occlusion

quantization

indexing

images of popular music CD's

database

scheme

background clutter

scheme

occlusion

indexing descriptors

scheme

local regions

indexing descriptors

vocabulary tree

local region descriptors

database with ground truth

retrieval

recognition quality

vocabulary tree approach

retrieval

recognition quality


Input: This paper presents a method for blind estimation of reverberation times in reverberant enclosures. The proposed algorithm is based on a statistical model of short-term log-energy sequences for echo-free speech. Given a speech utterance recorded in a reverberant room, it computes a Maximum Likelihood estimate of the room full-band reverberation time. The estimation method is shown to require little data and to perform satisfactorily. The method has been successfully applied to robust automatic speech recognition in reverberant environments by model selection. For this application, the reverberation time is first estimated from the reverberated speech utterance to be recognized. The estimation is then used to select the best acoustic model out of a library of models trained in various artificial re-verberant conditions. Speech recognition experiments in simulated and real reverberant environments show the efficiency of our approach which outperforms standard channel normaliza-tion techniques.

method

algorithm

estimation method

method

application

estimation

models

approach

blind estimation of reverberation times

robust automatic speech recognition

Speech recognition

reverberant enclosures

reverberant environments

reverberation time

reverberated speech utterance

artificial re-verberant conditions

simulated and real reverberant environments

statistical model of short-term log-energy sequences

Maximum Likelihood estimate of the room full-band reverberation time

model selection

acoustic model

channel normaliza-tion techniques

echo-free speech

method

blind estimation of reverberation times

statistical model of short-term log-energy sequences

algorithm

statistical model of short-term log-energy sequences

echo-free speech

method

robust automatic speech recognition

model selection

method

reverberated speech utterance

reverberation time

estimation

acoustic model

reverberant enclosures

blind estimation of reverberation times

reverberant environments

robust automatic speech recognition

artificial re-verberant conditions

models

simulated and real reverberant environments

Speech recognition

acoustic model

models

Speech recognition

approach

Speech recognition

channel normaliza-tion techniques

channel normaliza-tion techniques

approach


Input: Computer programs so far have not fared well in modeling language acquisition. For one thing, learning methodology applicable in general domains does not readily lend itself in the linguistic domain. For another, linguistic representation used by language processing systems is not geared to learning. We introduced a new linguistic representation, the Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB- -LSB- Zernik88 -RSB-, to facilitate language acquisition. From this, a language learning model was implemented in the program RINA, which enhances its own lexical hierarchy by processing examples in context. We identified two tasks: First, how linguistic concepts are acquired from training examples and organized in a hierarchy; this task was discussed in previous papers -LSB- Zernik87 -RSB-. Second, we show in this paper how a lexical hierarchy is used in predicting new linguistic concepts. Thus, a program does not stall even in the presence of a lexical unknown, and a hypothesis can be produced for covering that lexical gap.

Computer programs

hierarchy

program

language acquisition

language acquisition

learning methodology

linguistic representation

language processing systems

linguistic representation

language learning model

RINA

general domains

linguistic domain

Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB-

lexical hierarchy

linguistic concepts

lexical hierarchy

linguistic concepts

learning methodology

general domains

linguistic representation

language processing systems

linguistic representation

language acquisition

Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB-

language acquisition

lexical hierarchy

linguistic concepts

general domains

linguistic domain

Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB-

linguistic representation

language learning model

RINA

linguistic concepts

hierarchy


Input: This paper presents a novel ensemble learning approach to resolving German pronouns. Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. Experiments show that this approach is superior to a single decision-tree classifier. Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering and text summarisation.

ensemble learning approach

Boosting

classifiers

decision-tree classifier

standalone system

preprocessing modules

German pronouns

pronouns

approach

system

it

unannotated text

textual domain

manual annotation process

open-domain question answering

text summarisation

ensemble learning approach

German pronouns

standalone system

pronouns

standalone system

unannotated text

preprocessing modules

standalone system

preprocessing modules

manual annotation process

it

open-domain question answering

it

text summarisation

approach

decision-tree classifier

textual domain

open-domain question answering

pronouns

unannotated text

textual domain

system

open-domain question answering

text summarisation


Input: In this paper, we compare the performance of a state-of-the-art statistical parser -LRB- Bikel, 2004 -RRB- in parsing written and spoken language and in generating sub-categorization cues from written and spoken language. Although Bikel's parser achieves a higher accuracy for parsing written language, it achieves a higher accuracy when extracting subcategorization cues from spoken language. Our experiments also show that current technology for extracting subcategorization frames initially designed for written texts works equally well for spoken language. Additionally, we explore the utility of punctuation in helping parsing and extraction of subcategorization cues. Our experiments show that punctuation is of little help in parsing spoken language and extracting subcategorization cues from spoken language. This indicates that there is no need to add punctuation in transcribing spoken corpora simply in order to help parsers.

statistical parser

Bikel's parser

parsing

parsers

parsing written and spoken language

generating sub-categorization cues

parsing written language

extracting subcategorization frames

extraction of subcategorization cues

parsing spoken language

extracting subcategorization cues

written and spoken language

written and spoken language

written language

spoken language

written texts

spoken language

spoken language

spoken language

spoken corpora

accuracy

accuracy

it

technology

subcategorization cues

punctuation

punctuation

subcategorization cues

punctuation

statistical parser

parsing written and spoken language

statistical parser

generating sub-categorization cues

written and spoken language

generating sub-categorization cues

Bikel's parser

parsing written language

it

subcategorization cues

technology

extracting subcategorization frames

technology

spoken language

extracting subcategorization frames

written texts

punctuation

parsing

punctuation

extraction of subcategorization cues

spoken language

extracting subcategorization cues

parsing written and spoken language

generating sub-categorization cues

parsing spoken language

extracting subcategorization cues

accuracy

Bikel's parser

accuracy

it

subcategorization cues

spoken language

subcategorization cues

spoken language

written texts

spoken language


Input: This paper proposes an alignment adaptation approach to improve domain-specific -LRB- in-domain -RRB- word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.

alignment adaptation approach

alignment adaptation

statistical word alignment models

domain-specific -LRB- in-domain -RRB- word alignment

in-domain word alignment

domain-specific word alignment

domain-specific word alignment

out-of-domain corpus

large-scale out-of-domain corpus

small-scale in-domain corpus

models

approach

state-of-the-art technologies

precision

recall

relative error rate reduction

alignment adaptation approach

domain-specific -LRB- in-domain -RRB- word alignment

alignment adaptation

in-domain word alignment

out-of-domain corpus

alignment adaptation

large-scale out-of-domain corpus

statistical word alignment models

small-scale in-domain corpus

statistical word alignment models

models

domain-specific word alignment

approach

domain-specific word alignment

large-scale out-of-domain corpus

small-scale in-domain corpus

precision

recall

approach

state-of-the-art technologies

precision

approach

recall

approach

relative error rate reduction

approach

relative error rate reduction

state-of-the-art technologies


Input: With performance above 97% accuracy for newspaper text, part of speech -LRB- pos -RRB- tagging might be considered a solved problem. Previous studies have shown that allowing the parser to resolve pos tag ambiguity does not improve performance. However, for grammar formalisms which use more fine-grained grammatical categories, for example tag and ccg, tagging accuracy is much lower. In fact, for these formalisms, premature ambiguity resolution makes parsing infeasible. We describe a multi-tagging approach which maintains a suitable level of lexical category ambiguity for accurate and efficient ccg parsing. We extend this multi-tagging approach to the pos level to overcome errors introduced by automatically assigned pos tags. Although pos tagging accuracy seems high, maintaining some pos tag ambiguity in the language processing pipeline results in more accurate ccg supertagging.

accuracy

tagging accuracy

pos tagging accuracy

newspaper text

part of speech -LRB- pos -RRB- tagging

parsing

ccg parsing

parser

grammar formalisms

multi-tagging approach

multi-tagging approach

language processing pipeline

ccg supertagging

pos tag ambiguity

fine-grained grammatical categories

tag

ccg

premature ambiguity resolution

lexical category ambiguity

pos level

pos tags

pos tag ambiguity

formalisms

accuracy

part of speech -LRB- pos -RRB- tagging

newspaper text

part of speech -LRB- pos -RRB- tagging

tagging accuracy

grammar formalisms

parser

pos tag ambiguity

fine-grained grammatical categories

grammar formalisms

parsing

formalisms

multi-tagging approach

ccg parsing

multi-tagging approach

pos level

pos tag ambiguity

ccg supertagging

tag

fine-grained grammatical categories

ccg

fine-grained grammatical categories

lexical category ambiguity

multi-tagging approach

pos tag ambiguity

language processing pipeline


Input: We previously presented a framework for segmentation of complex scenes using multiple physical hypotheses for simple image regions. A consequence of that framework was a proposal for a new approach to the segmentation of complex scenes into regions corresponding to coherent surfaces rather than merely regions of similar color. Herein we present an implementation of this new approach and show example segmentations for scenes containing multi-colored piece-wise uniform objects. Using our approach we are able to intelligently segment scenes with objects of greater complexity than previous physics-based segmentation algorithms. The results show that by using general physical models we obtain segmentations that correspond more closely to coherent surfaces in the scene than segmentations found using only color.

framework

framework

approach

approach

approach

segmentation of complex scenes

segmentation of complex scenes

physical hypotheses

simple image regions

coherent surfaces

regions of similar color

segmentations

coherent surfaces

scenes

multi-colored piece-wise uniform objects

complexity

physics-based segmentation algorithms

physical models

framework

segmentation of complex scenes

physical hypotheses

framework

physical hypotheses

simple image regions

framework

approach

approach

segmentation of complex scenes

segmentations

scenes

coherent surfaces

regions of similar color

approach

physics-based segmentation algorithms

multi-colored piece-wise uniform objects

scenes


Input: SmartKom is a multimodal dialog system that combines speech, gesture, and mimics input and output. Spontaneous speech understanding is combined with the video-based recognition of natural gestures. One of the major scientific goals of SmartKom is to design new computational methods for the seamless integration and mutual disambiguation of multimodal input and output on a semantic and pragmatic level. SmartKom is based on the situated delegation-oriented dialog paradigm, in which the user delegates a task to a virtual communication assistant, visualized as a lifelike character on a graphical display. We describe the SmartKom architecture, the use of an XML-based markup language for multimodal content, and some of the distinguishing features of the first fully operational SmartKom demonstrator.

SmartKom

multimodal dialog system

SmartKom

computational methods

situated delegation-oriented dialog paradigm

SmartKom architecture

SmartKom demonstrator

speech

gesture

multimodal content

Spontaneous speech understanding

video-based recognition of natural gestures

integration and mutual disambiguation of multimodal input and output

semantic and pragmatic level

SmartKom

virtual communication assistant

graphical display

XML-based markup language

SmartKom

multimodal dialog system

speech

multimodal dialog system

gesture

multimodal dialog system

SmartKom

computational methods

computational methods

integration and mutual disambiguation of multimodal input and output

situated delegation-oriented dialog paradigm

SmartKom

XML-based markup language

multimodal content

speech

gesture

Spontaneous speech understanding

video-based recognition of natural gestures

semantic and pragmatic level

integration and mutual disambiguation of multimodal input and output


Input: We present a single-image highlight removal method that incorporates illumination-based constraints into image in-painting. Unlike occluded image regions filled by traditional inpainting, highlight pixels contain some useful information for guiding the inpainting process. Constraints provided by observed pixel colors, highlight color analysis and illumination color uniformity are employed in our method to improve estimation of the underlying diffuse color. The inclusion of these illumination constraints allows for better recovery of shading and textures by inpainting. Experimental results are given to demonstrate the performance of our method.

single-image highlight removal method

method

illumination-based constraints

occluded image regions

inpainting

highlight pixels

Constraints

pixel colors

highlight color analysis

illumination color uniformity

estimation of the underlying diffuse color

illumination constraints

recovery of shading and textures

image in-painting

inpainting process

inpainting

method

single-image highlight removal method

image in-painting

Constraints

method

method

estimation of the underlying diffuse color

illumination constraints

recovery of shading and textures

inpainting

recovery of shading and textures

illumination-based constraints

image in-painting

pixel colors

highlight color analysis

highlight color analysis

illumination color uniformity


Input: In this paper, we propose a novel method, called local non-negative matrix factorization -LRB- LNMF -RRB-, for learning spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose lo-calization constraint, in addition to the non-negativity constraint in the standard NMF -LSB- 1 -RSB-. This gives a set of bases which not only allows a non-subtractive -LRB- part-based -RRB- representation of images but also manifests localized features. An algorithm is presented for the learning of such basis components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.

method

algorithm

local non-negative matrix factorization -LRB- LNMF -RRB-

NMF

non-subtractive -LRB- part-based -RRB- representation of images

LNMF

NMF and PCA methods

LNMF

spatially localized, parts-based subspace representation of visual patterns

learning

face representation and recognition

objective function

lo-calization constraint

non-negativity constraint

localized features

method

spatially localized, parts-based subspace representation of visual patterns

objective function

lo-calization constraint

algorithm

learning

LNMF

face representation and recognition

NMF and PCA methods

face representation and recognition

non-negativity constraint

NMF

LNMF

NMF and PCA methods

face representation and recognition

LNMF


Input: Helping end users build and check process models is a challenge for many science and engineering fields. Many AI researchers have investigated useful ways of verifying and validating knowledge bases for ontologies and rules, but it is not easy to directly apply them to checking process models. Other techniques developed for checking and refining planning knowledge tend to focus on automated plan generation rather than helping users author process information. In this paper, we propose a complementary approach which helps users author and check process models. Our system, called KANAL, relates pieces of information in process models among themselves and to the existing KB, analyzing how different pieces of input are put together to achieve some effect. It builds interdepen-dency models from this analysis and uses them to find errors and propose fixes. Our initial evaluation shows that KANAL was able to find most of the errors in the process models and suggest useful fixes including the fixes that directly point to the sources of the errors.

process models

process models

process models

KANAL

process models

interdepen-dency models

KANAL

process models

science and engineering fields

checking process models

checking and refining planning knowledge

automated plan generation

knowledge bases

KB

ontologies

rules

errors

fixes

complementary approach

system

It

them

ontologies

rules

checking and refining planning knowledge

automated plan generation

complementary approach

process models

interdepen-dency models

It

them

errors

them

fixes


Input: In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque. After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%. It also gets a precision of 70% and a recall of 49% in the task of placing commas. Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.

machine learning techniques

comma checker

grammar checker

Basque

system

It

precision

recall

precision

recall

placing commas

machine learning techniques

comma checker

grammar checker

Basque

It

placing commas

comma checker

grammar checker

precision

system

recall

system

precision

It

recall

It


Input: The present paper reports on a preparatory research for building a language corpus annotation scenario capturing the discourse relations in Czech. We primarily focus on the description of the syntactically motivated relations in discourse, basing our findings on the theoretical background of the Prague Dependency Treebank 2.0 and the Penn Discourse Treebank 2. Our aim is to revisit the present-day syntactico-semantic -LRB- tectogrammatical -RRB- annotation in the Prague Dependency Treebank, extend it for the purposes of a sentence-boundary-crossing representation and eventually to design a new, discourse level of annotation. In this paper, we propose a feasible process of such a transfer, comparing the possibilities the Praguian dependency-based approach offers with the Penn discourse annotation based primarily on the analysis and classification of discourse connectives.

language corpus annotation scenario

Czech

Prague Dependency Treebank 2.0

Penn Discourse Treebank 2

Prague Dependency Treebank

discourse relations

syntactically motivated relations in discourse

syntactico-semantic -LRB- tectogrammatical -RRB- annotation

it

sentence-boundary-crossing representation

discourse level of annotation

Praguian dependency-based approach

Penn discourse annotation

analysis and classification of discourse connectives

language corpus annotation scenario

discourse relations

Prague Dependency Treebank 2.0

syntactically motivated relations in discourse

Penn Discourse Treebank 2

syntactically motivated relations in discourse

it

sentence-boundary-crossing representation

it

discourse level of annotation

Czech

discourse relations

Prague Dependency Treebank 2.0

Penn Discourse Treebank 2

syntactico-semantic -LRB- tectogrammatical -RRB- annotation

Prague Dependency Treebank

Penn discourse annotation

Praguian dependency-based approach

analysis and classification of discourse connectives

Praguian dependency-based approach

analysis and classification of discourse connectives

Penn discourse annotation


Input: Regression-based techniques have shown promising results for people counting in crowded scenes. However, most existing techniques require expensive and laborious data annotation for model training. In this study, we propose to address this problem from three perspectives: -LRB- 1 -RRB- Instead of exhaustively annotating every single frame, the most informative frames are selected for annotation automatically and actively. -LRB- 2 -RRB- Rather than learning from only labelled data, the abundant unlabelled data are exploited. -LRB- 3 -RRB- Labelled data from other scenes are employed to further alleviate the burden for data annotation. All three ideas are implemented in a unified active and semi-supervised regression framework with ability to perform transfer learning, by exploiting the underlying geometric structure of crowd patterns via manifold analysis. Extensive experiments validate the effectiveness of our approach.

Regression-based techniques

unified active and semi-supervised regression framework

transfer learning

manifold analysis

people counting in crowded scenes

data annotation

model training

annotation

data annotation

techniques

labelled data

approach

informative frames

geometric structure of crowd patterns

abundant unlabelled data

Regression-based techniques

people counting in crowded scenes

data annotation

techniques

data annotation

model training

unified active and semi-supervised regression framework

transfer learning

geometric structure of crowd patterns

unified active and semi-supervised regression framework

manifold analysis

geometric structure of crowd patterns

labelled data

abundant unlabelled data


Input: Representing images with layers has many important applications, such as video compression, motion analysis, and 3D scene analysis. This paper presents an approach to reliably extracting layers from images by taking advantages of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace. Layers in the input images will be mapped in the subspace, where it is proven that they form well-defined clusters and can be reliably identified by a simple mean-shift based clustering algorithm. Global optimality is achieved since all valid regions are simultaneously taken into account, and noise can be effectively reduced by enforcing the subspace constraint. Good layer descriptions are shown to be extracted in the experimental results.

Representing images with layers

mean-shift based clustering algorithm

applications

approach

video compression

motion analysis

3D scene analysis

Global optimality

layers

homographies

planar patches

scene

low dimensional linear subspace

Layers

subspace

clusters

regions

noise

subspace constraint

images

images

Representing images with layers

applications

approach

layers

images

clusters

mean-shift based clustering algorithm

clusters

subspace constraint

noise

video compression

applications

motion analysis

applications

3D scene analysis

applications

video compression

motion analysis

motion analysis

3D scene analysis

layers

images

planar patches

scene

Layers

images