A Method for Creating Structural Models of Text Documents Using Neural Networks.

The article describes modern neural network BERT-based models and considers their application for Natural Language Processing tasks such as question answering and named entity recognition. The article presents a method for solving the problem of automatically creating structural models of text documents. The proposed method is hybrid and is based on jointly utilizing several NLP models. The method builds a structural model of a document by extracting sentences that correspond to various aspects of the document. Information extraction is performed by using the BERT Question Answering model with questions that are prepared separately for each aspect. The answers are filtered via the BERT Named Entity Recognition model and used to generate the contents of each field of the structural model. The article proposes two algorithms for field content generation: Exclusive answer choosing algorithm and Generalizing answer forming algorithm, that are used for short and voluminous fields respectively. The article also describes the software implementation of the proposed method and discusses the results of experiments conducted to evaluate the quality of the method.The article describes modern neural network BERT-based models and considers their application for Natural Language Processing tasks such as question answering and named entity recognition. The article presents a method for solving the problem of automatically creating structural models of text documents. The proposed method is hybrid and is based on jointly utilizing several NLP models. The method builds a structural model of a document by extracting sentences that correspond to various aspects of the document. Information extraction is performed by using the BERT Question Answering model with questions that are prepared separately for each aspect. The answers are filtered via the BERT Named Entity Recognition model and used to generate the contents of each field of the structural model. The article proposes two algorithms for field content generation: Exclusive answer choosing algorithm and Generalizing answer forming algorithm, that are used for short and voluminous fields respectively. The article also describes the software implementation of the proposed method and discusses the results of experiments conducted to evaluate the quality of the method

Action Recognition in Tennis Videos using Optical Flow and Conditional Random Fields

The aim of Action Recognition is the automated analysis and interpretation of events in video sequences. As result of the applications that can be developed, and the widespread availability and popularization of digital video (security cameras, monitoring, social networks, among many other), this area is currently the focus of a strong and wide research interest in various domains such as video security, humancomputer interaction, patient monitoring and video retrieval, among others. Our long-term goal is to develop automatic action identification in video sequences using Conditional Random Fields (CRFs). In this work we focus, as a case of study, in the identification of a limited set of tennis shots during tennis matches. Three challenges have been addressed: player tracking, player movements representation and action recognition. Video processing techniques are used to generate textual tags in specific frames, and then the CRFs are used as a classifier to recognise the actions performed in those frames. The preliminary results appear to be quite promising.Sociedad Argentina de Informática e Investigación Operativ

Action Recognition in Tennis Videos using Optical Flow and Conditional Random Fields

The aim of Action Recognition is the automated analysis and interpretation of events in video sequences. As result of the applications that can be developed, and the widespread availability and popularization of digital video (security cameras, monitoring, social networks, among many other), this area is currently the focus of a strong and wide research interest in various domains such as video security, humancomputer interaction, patient monitoring and video retrieval, among others. Our long-term goal is to develop automatic action identification in video sequences using Conditional Random Fields (CRFs). In this work we focus, as a case of study, in the identification of a limited set of tennis shots during tennis matches. Three challenges have been addressed: player tracking, player movements representation and action recognition. Video processing techniques are used to generate textual tags in specific frames, and then the CRFs are used as a classifier to recognise the actions performed in those frames. The preliminary results appear to be quite promising.Sociedad Argentina de Informática e Investigación Operativ

Factors Affecting Part-of-Speech Tagging for Tagalog

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling

Conditional Random Fields (CRFs) constitute a popular and efficient approach for supervised sequence labelling. CRFs can cope with large description spaces and can integrate some form of structural dependency between labels. In this contribution, we address the issue of efficient feature selection for CRFs based on imposing sparsity through an L1 penalty. We first show how sparsity of the parameter set can be exploited to significantly speed up training and labelling. We then introduce coordinate descent parameter update schemes for CRFs with L1 regularization. We finally provide some empirical comparisons of the proposed approach with state-of-the-art CRF training strategies. In particular, it is shown that the proposed approach is able to take profit of the sparsity to speed up processing and hence potentially handle larger dimensional models