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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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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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Abstract:
With the advent of deep learning methods, researchers have been increasingly preferring deep learning methods over decades-old feature-engineering-inspired work in Natural Language Processing (NLP). The research community has been moving away from otherwise dominant feature engineering approaches; rather, is gravitating towards more complicated neural architectures. Highly competitive tools like part-of-speech taggers that exhibit human-like accuracy are traded off for complex networks, with the hope that the neural network will learn the features needed. In fact, there have been efforts to do NLP "from scratch" with neural networks that altogether eschew featuring engineering based tools (Collobert et al, 2011). In our research, we modify the input that is fed to neural networks by annotating the input with linguistic information: POS tags, Named Entity Recognition output, linguistic relations, etc. With just the addition of these linguistic features on a simple Siamese convolutional neural network, we are able to achieve state-of-the-art results. We argue that this strikes a better balance between feature vs. network engineering.
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Keyword:
Answer selection; CNN; Feature engineering; Natural Language Processing; neural networks; NLP; Question Answering
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URL: http://hdl.handle.net/10012/13570
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