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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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Abstract:
В работе приведены результаты экспериментов, проведенных с целью сравнительного анализа качества работы существующих облачных сервисов по обработке текстов на русском языке. В статье приводится обзор 10-ти облачных сервисов: TextRazor, RosetteTextAnalytics, EurekaEngine, CloudNaturalLanguage, Texterra, Pullenti, NER-ru, UDPipe, AOT, DeepPavlov. Количественные исследования качества работы выполнены для 6-ти из них. В процессе оценки сервисов анализировалось выполнение таких функций, как частеречная разметка, анализ тональности, распознавание именованных сущностей и категоризация текстов. Для сравнительной оценки качества работы сервисов были использованы материалы соревнований: factRuEval-2016 (выявление именованных сущностей), AlemResearch (тональность) и корпуса текстов, Taiga и OpenCorpora (морфологическая разметка). Качество распознавания именованных сущностей оценивалось путем расчета параметров Accuracy, Precision, Recall и F1. В результате проведенного исследования показано, что при решении задач ... : The paper presents the results of experiments conducted with the aim of a comparative analysis of the performance of the existing cloud services for natural language processing in Russian. The article provides an overview of 10 cloud services: TextRazor, RosetteTextAnalytics, EurekaEngine, CloudNaturalLanguage, Texterra, Pullenti, NER-ru, UDPipe, AOT, DeepPavlov. Quantitative studies of their performance were made for 6 of them. In the process of evaluating services, the execution of such functions as the part of speech tagging, sentiment analysis, named entity recognition and the categorization of texts were analyzed. For a comparative assessment of the quality of the services, the following competition materials were used: factRuEval-2016 (named entities), AlemResearch (sentiment) and the corpora, Taiga and OpenCorpora (part of speech). The named entities recognition quality was evaluated by calculating Accuracy, Precision, Recall, and F1 parameters. As a result of the study, it was shown that when solving ...
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Keyword:
Accuracy; Cloud services; Harmonic average; Named entity recognition; Natural language processing NLP; Natural language texts processing; Part of speech tagging; Precision; Recall; Sentiment analysis; Software as a service SaaS; Text categorization; Word processing; гармоническое среднее; категоризация текстов; морфологическая разметка; облачные сервисы; обработка естественно-языковых текстов; Обработка естественного языка ОЕЯ; программное обеспечение как сервис; распознавание именованных сущностей; тональность
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URL: https://dx.doi.org/10.25559/sitito.14.201804.872-880 http://sitito.cs.msu.ru/index.php/SITITO/article/view/453
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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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