label stringclasses 3
values | conf float64 0.34 1 | LLM verified stringclasses 2
values | iteration int64 -1 51 | filename stringlengths 9 123 | xpath stringlengths 19 26 ⌀ | text stringlengths 1 15.3k |
|---|---|---|---|---|---|---|
meta-data | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[1] | Pavan Kumar Perepu |
meta-data | 0.95 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[2] | Indian Institute of Information Technology, Sri City, India |
meta-data | 0.79 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[3] | Email: pavan.ppkumar@gmail.com |
text | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[4] | Mathematical expressions (MEs) have complex two-dimensional structures in which symbols can be present at any nested depth like superscripts, subscripts, above, below etc. As MEs are represented using LaTeX format, several text retrieval methods based on string matching, vector space models etc., have also been applied... |
text | 0.68 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[5] | keywords. Mathematical expression retrieval, Deep learning, Embedding, Recurrent neural network, Semantic similarity. |
text | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[6] | Mathematical expressions (MEs) form an essential part of the scientific and engineering documents. Scientific languages like LaTeX, MathML etc., can be used to encode and represent MEs. Given a mathematical expression in an encoding language, ME retrieval algorithm matches it against a database and extracts similar exp... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[8] | We have earlier applied longest common subsequence (LCS) algorithm [25] for matching and retrieval of similar LaTeX expressions. As LaTeX expressions cannot be directly matched, we have converted them into an integer encoding format in which LaTeX keywords are mapped to their corresponding integers. We have created a v... |
text | 0.97 | no | 51 | 2511.01364.pdf | /doclang[1]/text[10] | A simple and intuitive deep learning architecture for ME retrieval. As discussed later, this architecture considers variable-length input MEs without the need of padding them to a fixed size. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[11] | Semantic features for MEs are computed using a deep recurrent neural network (DRNN). That means, DRNN has been used for semantic feature extraction from MEs. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[12] | Matching and retrieval are performed based on the semantic features. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[14] | The rest of the paper is organized as follows: Section 2 focuses on the existing approaches for ME retrieval. In Section 3, we have presented our deep learning based ME retrieval algorithm. Section 4 discusses the experimental results and the paper is concluded in Section 5. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[15] | In [12, 38], authors have presented a detailed survey on ME recognition and retrieval methods. In [18], various math retrieval approaches based on text and tree models have been discussed. In text based approaches [1, 22, 43], linear encoding forms like LaTeX have been used and retrieval is based on bag of words and ve... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[16] | Few approaches [41, 42] are based on content-based image retrieval, where queries are taken in the form of images. In these approaches, an ME image is taken as input, segmented, features (like density, contours etc.) are extracted and matched. A Tangent search engine [30, 39, 40] has been proposed based on layout of sy... |
text | 0.99 | no | 51 | 2511.01364.pdf | /doclang[1]/text[17] | Recently, word embedding approaches [5, 9, 21, 28] proposed for text retrieval based on semantic similarity have also been applied for ME retrieval. In [31], authors have focused on the embedding of mathematical concepts and types to retrieve mathematical information from scientific documents. In [15], unsupervised met... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[18] | In [4], text word embedding approaches have been used for various retrieval tasks like math query expansion and similarity, extraction of semantic knowledge etc. In [29], authors have proposed to learn math embedding using graph convolutional neural networks for semantic search of MEs. In [3], multimodal image and grap... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[19] | In [7], formula embedding has been obtained using BERT models and based on cosine similarity between embeddings, top k MEs have been retrieved. In [33], authors have focused on the generation of embeddings from expression operator trees and vice-versa, which have been used in their formula retrieval framework. Few othe... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[20] | In our present work, we have proposed a simple and intuitive deep learning architecture for feature extraction and retrieval of MEs. We have earlier proposed an LCS based algorithm for ME retrieval [25]. In this approach [25, 26], LaTeX expressions are preprocessed and converted into an integer encoding format. A vocab... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[23] | As shown in Fig. 1, proposed deep learning model for ME retrieval has several layers. As discussed earlier, ME in the integer encoded format is given as input to the model. As MEs vary in lengths (number of symbols), model should consider the variable-length inputs. However, fully connected dense layers in any deep lea... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[24] | Input to the model is an integer-encoded ME. Let its length be l ( l may be different for different MEs). |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[25] | Embedding operation [21] is applied on the input ME so that each integer symbol in the input is mapped to a e -dimensional vector. If the length of the input ME is l , output of this embedding layer is of size, ( l × e ). Embedding layer serves as look-up table that maps each symbol in the Vocabulary to a e -dimensiona... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[26] | All the l embedding vectors (of size, e ) are fed to a recurrent neural network (RNN) with one layer. If RNN layer has t neurons, t outputs are obtained for each embedding vector. As there are l embedding vectors, output of the RNN layer is of size, ( l × t ). |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[27] | As l is a variable, we have added a global pooling layer to obtain fixed size output before applying to the final fully connected dense layer. Input to the global pooling layer is of size, l × t (output from the previous RNN layer). This layer applies a global pooling (maximum, average or minimum) operation [10] on eac... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[28] | The fixed length output from the global pooling layer is fed as input to the final fully connected dense layer. The number of neurons in this layer depends on the number of classes in the auxiliary classification task chosen to train this model. As mentioned earlier, classification can be based on complexity, field etc... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[29] | Here, embedding vector size ( e ), RNN variant (like Vanilla RNN, GRU, LSTM [10]etc.) and number of neurons in RNN layer ( t ) are the design parameters that can be tuned based on the model performance. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[30] | For our earlier works on the recognition and retrieval of MEs [25, 27], we have created our PACME database of 829 ME images from different fields like Algebra, Trigonometry, Calculus etc., along with corresponding LaTeX ground truth [23]. This database consists of different types of MEs, ranging from simple to complex ... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[31] | Based on the complexity, we have partitioned this LaTeX database into three classes with the following labels: Simple, Medium and Complex. As discussed in Section 1, complexity is quantified in terms of nested depth. Simple expressions do not have nested symbols, Medium expressions have nested symbols with depth one wh... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[32] | We have divided the database of 829 LaTeX expressions into training and test sets, in the ratio, 70:30. Further, 20% of the training samples have been used for validation during the training process [10]. We have implemented the model using Python Keras library [6]. In our model, we have used LSTM variant for the RNN l... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[34] | The final fully connected dense layer has a number of neurons which is equal to the number of classes so that neuron, i , corresponds to a class, i . Neuron, i , gives the probability of class, i , using a standard Softmax activation function [10]. If there are c neurons which correspond to c classes, then the probabil... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[35] | Here, out k is the output score for neuron, k , obtained by the linear combination of its inputs and weights. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[37] | After the model has been trained, any integer encoded ME can be fed to it and the output after the global pooling layer, which is of size, 64, is obtained. This 64-dimensional output forms the feature vector for the given input integer encoded ME. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[38] | Feature extraction using the deep learning model has been illustrated on the earlier mentioned example ME, in Table 2. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[39] | As mentioned in the previous subsection, feature vectors are similarly computed for each database ME to generate a feature database. Feature vector of a query is then matched with the feature vectors of all the database MEs using the euclidean distance, and top k matches are retrieved. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[40] | As matching is based on the euclidean distance, it takes linear time, O ( t ), where t is the size of the feature vector. But LCS based matching has quadratic time complexity, O ( n 2 ) where n is the number of symbols in the input integer encoded ME [25]. If there are T database MEs, the complete retrieval process usi... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[41] | For the experimentation, we have applied our proposed deep learning model on our PACME LaTeX database [23]. However, our model provides a general framework for the retrieval of any other unstructured text (like MathML, HTML etc.) by just defining the corresponding vocabulary. |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[42] | In Table 3, we have shown an ME query (Integral expression) in the first row. As discussed earlier, it is converted to the integer encoding format, fed to the trained deep learning model and then features are obtained (from the output of global pooling layer). These features are compared with those of database MEs usin... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[45] | From the above experiments, it can be concluded that deep learning based ME retrieval algorithm focuses on the semantic similarity. Further, this deep learning based retrieval framework is also flexible as any type of semantic notion can be incorporated by choosing a classification task based on the user requirements (... |
text | 1 | no | 51 | 2511.01364.pdf | /doclang[1]/text[46] | In our present work, we have focused on deep learning approaches for ME retrieval based on semantic similarity. This semantic based retrieval approach is also useful for math plagiarism detection. Our deep learning model considers variable-length MEs without padding. This model has been initially trained on an auxiliar... |
reference | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[47] | Adeel, M., Cheung, H. S., and Khiyal, A. H. (2008). Math go! prototype of a content based mathematical formula search engine. Journal of Theoretical and Applied Information Technology , 4(10):1002-1012. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[48] | Adeel, M., Sher, M., and Khiyal, M. S. H. (2012). Efficient cluster-based information retrieval from mathematical markup documents. World Applied Sciences Journal , 17:611-616. |
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reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[61] | Krstovski, K. and Blei, D. M. (2018). Equation embeddings. ArXiv , abs/1803.09123. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[62] | Li, R., Wang, J., and Tian, X. (2023a). A multi-modal retrieval model for mathematical expressions based on convnext and hesitant fuzzy set. Electronics , 12:4363. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[64] | Mansouri, B., Rohatgi, S., Oard, D. W., Wu, J., Giles, C. L., and Zanibbi, R. (2019a). Tangent-cft: An embedding model for mathematical formulas. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval , pages 11-18. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[65] | Mansouri, B., Zanibbi, R., and Oard, D. W. (2019b). Characterizing searches for mathematical concepts. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) , pages 57-66. IEEE. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[66] | Mansouri, B., Zanibbi, R., and Oard, D. W. (2021). Learning to rank for mathematical formula retrieval. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , page 952-961. Association for Computing Machinery. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[67] | Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 , NIPS'13, pages 3111 - 3119, Red Hook, NY, USA. Curran Associates ... |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[68] | Miner, R. and Munavalli, R. (2006). Mathfind: A math-aware search engine. In Proceedings of the International Conference on Information Retrieval , pages 735-735, New York, USA. |
reference | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[69] | Pavan Kumar, P. (2018). PACME - Mathematical Expression Image Database and its LaTeX Ground Truth. https://sites.google.com/site/ppkumarcs/datasets . |
reference | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[70] | Pavan Kumar, P. (2021). Openmp implementation of parallel longest common subsequence algorithm for mathematical expression retrieval. Parallel Processing Letters , 31(02):2150007. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[71] | Pavan Kumar, P., Agarwal, A., and Bhagvati, C. (2012). A structure based approach for mathematical expression retrieval. In Sombattheera, C., Loi, N. K., Wankar, R., and Quan, T., editors, Multi-disciplinary Trends in Artificial Intelligence , pages 23-34, Berlin, Heidelberg. Springer Berlin Heidelberg. |
reference | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[72] | Pavan Kumar, P., Agarwal, A., and Bhagvati, C. (2014). A string matching based algorithm for performance evaluation of mathematical expression recognition. Sadhana , 39(1):63 - 79. |
reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[73] | Pavan Kumar, P., Agarwal, A., and Bhagvati, C. (2018). Isolated structural error analysis of printed mathematical expressions. Pattern Analysis and Applications , 21(4):1097 - 1107. |
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reference | 0.99 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[76] | Stalnaker, D. and Zanibbi, R. (2015). Math expression retrieval using an inverted index over symbol pairs. In Document recognition and retrieval XXII , volume 9402, page 940207. International Society for Optics and Photonics. |
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reference | 1 | yes | 51 | 2511.01364.pdf | /doclang[1]/text[90] | Zhong, W. and Zanibbi, R. (2019). Structural similarity search for formulas using leaf-root paths in operator subtrees. Proceedings of the European Conference on Information Retrieval (ECIR) . |
meta-data | 0.79 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[1] | Muneer Ahmad 1 , Dr. M. Sadik Batcha 2 , Wasim Rashid 3 , Obaid Hafiz 4 |
meta-data | 0.84 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[3] | Ph.D Research Scholar, Department of Library and Information Science, Annamalai University, Tamil Nadu, (India) |
meta-data | 0.74 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[4] | 2 Associate Professor, Department of Library and Information Science, |
meta-data | 0.91 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[7] | 4 Library Professional, Central University of Kashmir, Nowgam Srinagar, Jammu & Kashmir, India |
text | 0.91 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[8] | This paper examines and explores the web impact factor through a webometric study of the present 12 University Websites of Jammu and Kashmir. Identifies the domain systems of the websites; analyzes the number of web pages and link pages, and calculates the External Link WIF or simple web ... |
text | 0.99 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[10] | Webometric is an attractive and one of the most relevant info-metric sub-discipline among traditional bibliometrics approaches. The availability of reliable web data is important to achieve standard results, despite of tool related technical problems and intermediaries requires for web data recovery. The h... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[11] | The WIF is generally defined as the ratio between the number of links received and the total number of web pages of a particular website (Idrees, 2015)[5]. There are three types of link namely, outlinks or external links which are HTML code on the website which allows site visitors to access other websites, inlinks or ... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[12] | Where, the variable X represents the number of outlinks, Y represents the total backlink, Z represents the total self-link count and P represents the number of web pages published on the websites which are indexed by a search engine. |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[13] | However, Noruzi's research shown that self-links for the website under evaluation can provide ambiguous results, as the number of self-links can be manipulated by different means by site owners. For example, in some cases, self-link counts increase because of email addresses associated with websites, which... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[14] | more visibility on the web and potentially more traffic to the site |
text | 0.99 | no | 51 | 2511.01496.pdf | /doclang[1]/text[15] | better coverage by search engines, and |
text | 0.98 | no | 51 | 2511.01496.pdf | /doclang[1]/text[16] | higher ranking in search results |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[17] | The RWIF is the result of excluding self-links for a website, thus establishing an analogous impact factor (Idrees, 2015)[5]. So in this study, the authors calculated the RWIF for websites in Jammu and Kashmir Universities as shown below: |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[18] | Where, the variable X represents the inlinks (external backlinks) to the website and Y represents the number of web pages published on the websites which are indexed by a search engine. |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[19] | The details of all the universities are listed in Table 1, which gives a brief sketch of all 12 universities in Jammu and Kashmir with their year of establishment, located city/ town with URL. |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[20] | In 1955, Eugene Garfield introduced the term impact factor to measure the overall influence of a journal's articles on later literature, subsequently calling it journal impact factor (JIF). JIF is a measure of frequency that reflects the average number of citations received by a journal article ... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[21] | The primary objective of this study is to examine the WIF for websites of Jammu and Kashmir. Other specific objectives include: |
text | 0.96 | no | 51 | 2511.01496.pdf | /doclang[1]/text[23] | To study the growth of Universities websites in Jammu and Kashmir |
text | 0.88 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[24] | To calculate the Revise Web impact factor (RWIF) and Internal Web Impact Factor for the Jammu and Kashmir university websites |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[26] | This study also undertakes link analysis for the inlinks to websites of the selected Jammu and Kashmir universities. In doing so, the total number of inlinks is calculated, which refers to a particular website anywhere in its text body. |
text | 0.94 | no | 51 | 2511.01496.pdf | /doclang[1]/text[27] | The present study makes a webometric analysis of University websites in Jammu and Kashmir. The study examined the websites of 12 universities in the state and aimed at to establish a kind of academic ranking of these websites by measuring their web impact factor. The ranking of websites will help the rea... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[29] | The research followed the descriptive approach. The research method used in this study is survey method. In order to collect data, we have used the list of universities in Jammu and Kashmir provided by the University Grant Commission of India (www.ugc.ac.in). Before using the list, we have checked the acce... |
text | 0.64 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[30] | In present study, therefore, the authors decided to choose Google and Yahoo search engines and Small Seo Tool and Seo Chat application to collect data, i.e. number of web pages in a university website, number of external backlinks and outlinks or external links. |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[32] | Table 3 shows the domain-wise distribution of the websites of Jammu and Kashmir Universities. Out of 12 Universities, 5 (41.67%) are having '.ac.in' domain, 2 (16.67%) Universities has '.edu.in', 2(16.67%) has '.in' domain and remaining universities have used different domains, one university used '.org'... |
text | 0.99 | no | 51 | 2511.01496.pdf | /doclang[1]/text[33] | Table 4 shows the distribution of Internal-link Web Impact Factor of websites of universities in Jammu and Kashmir. From the table, it is obvious that Cluster University of Jammu is in the first position with 0.9018 of internal links Web Impact Factor. Shri Mata Vaishno Devi University has the second place with 0.... |
text | 0.62 | yes | 51 | 2511.01496.pdf | /doclang[1]/text[34] | 0.2123, and Jammu University occupies the fifth place with 0.0874. Central University of Kashmir, Sher-eKashmir Institute of Medical Science, Sher-e-Kashmir University of Agricultural Science & Technology and Kashmir University are having sixth, seventh, eighth and ninth positions respectively. No Data a... |
text | 0.99 | no | 51 | 2511.01496.pdf | /doclang[1]/text[36] | Web Impact Factor and link analysis of Universities websites of Jammu and Kashmir is an unexplored area of webometric research. The present study, hopefully, provides a fair idea and information about the website of all the 12 university websites of J & K. There is a scope for further webometric research... |
text | 1 | no | 51 | 2511.01496.pdf | /doclang[1]/text[38] | The 4 universities each 2 (16.67) is having .edu.in and .in domain for their university websites. |
reference | 0.94 | no | 51 | 2511.01496.pdf | /doclang[1]/text[39] | Cluster University of Jammu ranked 1 (0.9018) in Internal Link web impact factor of university websites in Jammu and Kashmir. |
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This repository contains the models used for docling-nlp.
Contents
This model repository packages the pretrained assets used by Docling’s NLP components:
- CRF models for material classification and English part-of-speech tagging
- fastText models for language detection, metadata, semantic, topic, and person-name classification
- Regular-expression assets for geographic-location extraction and unit handling
- A default tokenizer model
Correct workflow to add new files
git lfs status
git commit -s -m "Add new models ..."
git lfs push origin main
git push origin main
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