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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 20 of 20 1 Linguistic resources for paraphrase generation in Portuguese: a Lexicon-Grammar approach Barreiro, Anabela; Mota, Cristina; Baptista, Jorge... In: ISSN: 1574-020X ; EISSN: 1574-0218 ; Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-03548861 ; Language Resources and Evaluation, Springer Verlag, 2022, ⟨10.1007/s10579-021-09561-5⟩ ; https://link.springer.com/article/10.1007/s10579-021-09561-5 (2022) BASE Show details 2 Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model Ying Zhou; Xiaokang Hu; Vera Chung In: Applied Sciences; Volume 12; Issue 1; Pages: 499 (2022) BASE Show details 3 Using Alignments in Automatic Paraphrase Production to Combat Data Sparsity in Question Interpretation for a Virtual Patient Dialogue System Ewing, Sarah. - : The Ohio State University, 2019 BASE Show details 4 Question Paraphrase Generation for Question Answering System Qin, Haocheng. - : University of Waterloo, 2015 BASE Show details 5 A Computational Approach to the Analysis and Generation of Emotion in Text Keshtkar, Fazel. - 2011 BASE Show details 6 A Computational Approach to the Analysis and Generation of Emotion in Text Keshtkar, Fazel. - 2011 Abstract: Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs. Keyword: Authoring Tool; Bootstrapping; Emotion Analysis; Natural Language; Natural Language Generation; Paraphrase; Processing; Sentiment Orientation URL: http://hdl.handle.net/10393/20137 BASE Hide details 7 A Computational Approach to the Analysis and Generation of Emotion in Text ... Keshtkar, Fazel. - : Université d'Ottawa / University of Ottawa, 2011 BASE Show details 8 A Computational Approach to the Analysis and Generation of Emotion in Text Keshtkar, Fazel. - : Université d'Ottawa / University of Ottawa, 2011 BASE Show details 9 Contextualizing semantic representations using syntactically enriched vector models Thater, Stefan; Fürstenau, Hagen; Pinkal, Manfred In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 2, 948-957 BLLDB Show details 10 The Circle of Meaning: From Translation to Paraphrasing and Back Madnani, Nitin. - 2010 BASE Show details 11 Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models Marton, Yuval Yehezkel. - 2009 BASE Show details 12 A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar Gardent, Claire; Kow, Eric In: 45th Annual Meeting of the Association for Computational Linguistics - ACL 2007 ; https://hal.inria.fr/inria-00149366 ; 45th Annual Meeting of the Association for Computational Linguistics - ACL 2007, Jun 2007, Prague, Czech Republic. pp.328-335 (2007) BASE Show details 13 Algoritm tekstovych preobrazovanij v sisteme smyslovoj kompressii Karpilovič, Tatʹjana P. In: Minskij Gosudarstvennyj Lingvističeskij Universitet. Vestnik MGLU. Serija 1, Filologija = Series 1, Philology. - Minsk : MGLU (2004) 13, 142-152 BLLDB Show details 14 Automatische Generierung natürlichsprachlicher Paraphrasen formaler Bedeutungsbeschreibungen Küstner, Herbert In: Semantik, Lexikographie und Computeranwendungen. - Tübingen : Niemeyer (1996), 307-315 BLLDB Show details 15 Generating sentences from different perspectives Fedder, Lee In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 5 (1991), 125-130 BLLDB Show details 16 Parrot: The Janus Paraphraser MacLaughlin, Dawn M. In: DTIC AND NTIS (1989) BASE Show details 17 Zur automatischen Paraphrasierung natuerlich-sprachlicher Saetze Habel, Christopher; Schmidt, Arno; Schweppe, Heinz In: Akten des 12. Linguistischen Kolloquiums ; 1. Wortstellung und Bedeutung. - Tübingen : Niemeyer (1978), 239-248 BLLDB Show details 18 THE TEACHER MODE OF THE SENTENCE FAIRY SYSTEM: HOW TO CREATE YOUR OWN E-LEARNING WRITING LESSONS FOR GERMAN ELEMENTARY SCHOOL PUPILS Karin Harbusch; Christine Franz; Ulrich Koch In: http://userpages.uni-koblenz.de/~harbusch/ICERI-2012.pdf BASE Show details 19 Experience Nitin Madnani In: http://www.desilinguist.org/pdf/madnani-cv.pdf BASE Show details 20 Exploring neural paraphrasing to improve fluency of rule-based generation Du, Shixiao BASE Show details

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