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Navegación de corpus a través de anotaciones lingüísticas automáticas obtenidas por Procesamiento del Lenguaje Natural: de anecdótico a ecdótico
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In: Revista de Humanidades Digitales, vol. 4, pp. 136 (2019)
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A Combined CNN and LSTM Model for Arabic Sentiment Analysis
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In: Lecture Notes in Computer Science ; 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-02060041 ; 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.179-191, ⟨10.1007/978-3-319-99740-7_12⟩ (2018)
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Dialog Acts Annotations for Online Chats ; Annotation en Actes de Dialogue pour les Conversations d’Assistance en Ligne
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In: Actes TALN-RECITAL 2018 ; 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN) ; https://hal.archives-ouvertes.fr/hal-01943345 ; 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN), 2018, Rennes, France (2018)
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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2018, Avignon,France)
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In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; 9th International Conference of the CLEF Association (CLEF 2018) ; https://hal.archives-ouvertes.fr/hal-03044243 ; Bellot, Patrice; Trabelsi, Chiraz; Mothe, Josiane; Murtagh, Fionn; Nie, Jian-Yun; Soulier, Laure; Sanjuan, Eric; Cappellato, Linda; Ferro, Nicola. 9th International Conference of the CLEF Association (CLEF 2018), Sep 2018, Avignon, France. Lecture Notes in Computer Science, Springer Berlin / Heidelberg; Springer, 2018, Experimental IR Meets Multilinguality, Multimodality, and Interaction, 978-3-319-98931-0. ⟨10.1007/978-3-319-98932-7⟩ ; https://link.springer.com/book/10.1007%2F978-3-319-98932-7 (2018)
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From Emoji Usage to Categorical Emoji Prediction
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In: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018) ; https://hal-amu.archives-ouvertes.fr/hal-01871045 ; 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018), Mar 2018, Hanoï, Vietnam ; https://www.cicling.org/2018/ (2018)
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Prediction of Psychosis Using Big Web Data in the United States
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In: http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169 (2018)
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An Empirical Study of Word Embedding Dimensionality Reduction ...
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An Empirical Study of Word Embedding Dimensionality Reduction ...
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Data-Driven Language Understanding for Spoken Dialogue Systems ...
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ОБЛАЧНЫЕ СЕРВИСЫ ДЛЯ ОБРАБОТКИ ТЕКСТОВ НА ЕСТЕСТВЕННОМ ЯЗЫКЕ ... : CLOUD SERVICES FOR NATURAL LANGUAGE PROCESSING ...
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Automatic Annotation And Retrieval System (Ilars) For Enhancing Organizational E-Learning ...
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Automatic Annotation And Retrieval System (Ilars) For Enhancing Organizational E-Learning ...
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Proposition-based summarization with a coherence-driven incremental model
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Fang, Yimai. - : University of Cambridge, 2018. : Computer Science and Technology, 2018. : Hughes Hall, 2018
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Abstract:
Summarization models which operate on meaning representations of documents have been neglected in the past, although they are a very promising and interesting class of methods for summarization and text understanding. In this thesis, I present one such summarizer, which uses the proposition as its meaning representation. My summarizer is an implementation of Kintsch and van Dijk's model of comprehension, which uses a tree of propositions to represent the working memory. The input document is processed incrementally in iterations. In each iteration, new propositions are connected to the tree under the principle of local coherence, and then a forgetting mechanism is applied so that only a few important propositions are retained in the tree for the next iteration. A summary can be generated using the propositions which are frequently retained. Originally, this model was only played through by hand by its inventors using human-created propositions. In this work, I turned it into a fully automatic model using current NLP technologies. First, I create propositions by obtaining and then transforming a syntactic parse. Second, I have devised algorithms to numerically evaluate alternative ways of adding a new proposition, as well as to predict necessary changes in the tree. Third, I compared different methods of modelling local coherence, including coreference resolution, distributional similarity, and lexical chains. In the first group of experiments, my summarizer realizes summary propositions by sentence extraction. These experiments show that my summarizer outperforms several state-of-the-art summarizers. The second group of experiments concerns abstractive generation from propositions, which is a collaborative project. I have investigated the option of compressing extracted sentences, but generation from propositions has been shown to provide better information packaging.
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Keyword:
abstractive summarisation; abstractive summarization; coherence; computational linguistics; document understanding; natural language processing; NLP; summarisation; summarization; text understanding
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URL: https://www.repository.cam.ac.uk/handle/1810/287468 https://doi.org/10.17863/CAM.34773
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NLP Corpus Observatory – Looking for Constellations in Parallel Corpora to Improve Learners’ Collocational Skills
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In: Schneider, Gerold; Graën, Johannes (2018). NLP Corpus Observatory – Looking for Constellations in Parallel Corpora to Improve Learners’ Collocational Skills. In: 7th Workshop on NLP for Computer Assisted Language Learning at SLTC 2018 (NLP4CALL 2018), Stockholm, 7 November 2018 - 7 November 2018, 69-78. (2018)
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Simple Convolutional Neural Networks with Linguistically-Annotated Input for Answer Selection in Question Answering
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