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Academic literature on the topic 'Dialogue corpus'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Dialogue corpus"

1

Wang, Jiancheng, Jingjing Wang, Changlong Sun, et al. "Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9177–84. http://dx.doi.org/10.1609/aaai.v34i05.6454.

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Sentiment analysis in dialogues plays a critical role in dialogue data analysis. However, previous studies on sentiment classification in dialogues largely ignore topic information, which is important for capturing overall information in some types of dialogues. In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. Specifically, we propose a topic-aware multi-task learning (TML) approach which learns topic-enriched

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Lowe, Ryan, Nissan Pow, Iulian Vlad Serban, Laurent Charlin, Chia-Wei Liu, and Joelle Pineau. "Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus." Dialogue & Discourse 8, no. 1 (2017): 31–65. http://dx.doi.org/10.5087/dad.2017.102.

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In this paper, we construct and train end-to-end neural network-based dialogue systems usingan updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maxim

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3

Walker, M. A. "An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email." Journal of Artificial Intelligence Research 12 (June 1, 2000): 387–416. http://dx.doi.org/10.1613/jair.713.

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This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named E

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Tenbrink, Thora, Elena Andonova, Gesa Schole, and Kenny R. Coventry. "Communicative Success in Spatial Dialogue: The Impact of Functional Features and Dialogue Strategies." Language and Speech 60, no. 2 (2016): 318–29. http://dx.doi.org/10.1177/0023830916651097.

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This paper addresses the impact of dialogue strategies and functional features of spatial arrangements on communicative success. To examine the sharing of cognition between two minds in order to achieve a joint goal, we collected a corpus of 24 extended German-language dialogues in a referential communication task that involved furnishing a dolls’ house. Results show how successful communication, as evidenced by correct placement of furniture items, is affected by: (a) functionality of the furniture arrangement; (b) previous task experience; and (c) dialogue features such as description length

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Walker, M. A., I. Langkilde-Geary, H. Wright Hastie, J. Wright, and A. Gorin. "Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System." Journal of Artificial Intelligence Research 16 (May 1, 2002): 293–319. http://dx.doi.org/10.1613/jair.971.

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Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the syste

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Zhu, Qi, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang. "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset." Transactions of the Association for Computational Linguistics 8 (July 2020): 281–95. http://dx.doi.org/10.1162/tacl_a_00314.

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To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across dom

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Higashinaka, Ryuichiro, Kotaro Funakoshi, Masahiro Araki, Hiroshi Tsukahara, Yuka Kobayashi, and Masahiro Mizukami. "Text Chat Dialogue Corpus Construction and Analysis of Dialogue Breakdown." Journal of Natural Language Processing 23, no. 1 (2016): 59–86. http://dx.doi.org/10.5715/jnlp.23.59.

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YANO, HIROYUKI, and AKIRA ITO. "Construction of Corpus for Emergent Dialogue." Journal of Natural Language Processing 6, no. 4 (1999): 117–37. http://dx.doi.org/10.5715/jnlp.6.4_117.

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LITMAN, DIANE, and KATE FORBES-RILEY. "Correlations between dialogue acts and learning in spoken tutoring dialogues." Natural Language Engineering 12, no. 2 (2006): 161–76. http://dx.doi.org/10.1017/s1351324906004165.

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We examine correlations between dialogue behaviors and learning in tutoring, using two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. To formalize the notion of dialogue behavior, we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain. A unigram analysis of our annotated data shows that student learning correlates both with the tutor's dialogue acts and with the student's dialogue acts. A bigram analysis shows that student learning also correlates with joint patterns of tutor and student dialogue

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Ales, Zacharie, Alexandre Pauchet, and Arnaud Knippel. "Extraction and Clustering of Two-Dimensional Dialogue Patterns." International Journal on Artificial Intelligence Tools 27, no. 02 (2018): 1850001. http://dx.doi.org/10.1142/s021821301850001x.

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This article proposes a two-step methodology to ease the identification of dialogue patterns in a corpus of annotated dialogues. The annotations of a given dialogue are represented within a two-dimensional array whose lines correspond to the utterances of the dialogue ordered chronologically. The first step of our methodology consists in extracting recurrent patterns. To that end, we adapt a dynamic programming algorithm used to align two-dimensional arrays by reducing its complexity and improving its trace-back procedure. During the second step, the obtained patterns are clustered using vario

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Dissertations / Theses on the topic "Dialogue corpus"

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Inagaki, Yasuyoshi, Nobuo Kawaguchi, Shigeki Matsubara, and Koichiro Ryu. "Bilingual Speech Dialogue Corpus for Simultaneous Machine Interpretation Research." The oriental chapter of COCOSDA (The International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques), 2003. http://hdl.handle.net/2237/15076.

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Domínguez, Mujica Carmen Luisa. "Dialogue adulte-enfant : un essai de compréhension." Paris 5, 1985. http://www.theses.fr/1985PA05H038.

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Fréchet, Anne-Lise. "Analyse linguistique d'un corpus de dialogue homme-machine (oral finalisé)." Paris 3, 1992. http://www.theses.fr/1993PA030074.

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Cette these presente la constitution d'un corpus de dialogue homme-machine et les etapes de l'analyse lexicale en vue de la constitution du vocabulaire de l7application. Nous presentons quelques aspects syntaxiques et pragmatiques du dialogue dans la perspective de la communication homme-machine multimodale. Nous definissons le plan d'activite de l'utilisateur autour duquel nous replacons ses actes de langage et ses actes gestuels. Nous decrivons ses etats intentionnels et ses attitudes cognitives, en liaison avec les marqueurs pragmatiques du discours. Notre hypothese est qu'il est possible d

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Matsubara, Shigeki, Tomohiro Ohno, and Yuki Kamiya. "Coherent Back-Channel Feedback Tagging of In-Car Spoken Dialogue Corpus." ACL(Association for computational linguistics), 2010. http://aclweb.org/anthology/.

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Inagaki, Yasuyoshi, Fumitada Itakura, Kazuya Takeda, et al. "An Advanced Japanese Speech Corpus for In-car Spoken Dialogue Research." The oriental chapter of COCOSDA (The International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques), 2003. http://hdl.handle.net/2237/15463.

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Arias, Badia Blanca. "Television dialogue and subtitling: a corpus-driven study of police procedurals." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404733.

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The specialised literature has suggested the intermediate position of television dialogue and subtitling along the continuum from spoken to written language (e.g. Díaz-Cintas 2003; Quaglio 2009; Forchini 2012). This dissertation adopts a corpus-driven methodology to tackle this issue from a descriptive, contrastive point of view. It reports on results of the analysis of the Corpus of Police Procedurals (CoPP), a corpus containing the transcribed dialogue (EN) and the DVD subtitling (ES) of fifteen episodes of three contemporary police procedurals, namely Dexter (Showtime, 2006), The Mentalist

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Hunter, Gordon James Allan. "Statistical language modelling of dialogue material in the British national corpus." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446734/.

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Statistical language modelling may not only be used to uncover the patterns which underlie the composition of utterances and texts, but also to build practical language processing technology. Contemporary language applications in automatic speech recognition, sentence interpretation and even machine translation exploit statistical models of language. Spoken dialogue systems, where a human user interacts with a machine via a speech interface in order to get information, make bookings, complaints, etc., are example of such systems which are now technologically feasible. The majority of statistic

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Inagaki, Yasuyoshi, Nobuo Kawaguchi, Shigeki Matsubara, and Tomohiro Ohno. "SPIRAL CONSTRUCTION OF SYNTACTICALLY ANNOTATED SPOKEN LANGUAGE CORPUS." IEEE, 2003. http://hdl.handle.net/2237/15085.

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Inagaki, Yasuyoshi, Nobuo Kawaguchi, Shigeki Matsubara, Yukiko Yamaguchi, Yuki Irie, and Itsuki Kishida. "Construction of an Advanced In-Car Spoken Dialogue Corpus and its Characteristic Analysis." ISCA(International Speech Communication Association), 2004. http://hdl.handle.net/2237/15452.

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Kawaguchi, Nobuo, Yukiko Yamaguchi, Shigeki Matsubara, and Shingo Kato. "Dialogue Structure Annotation of In-car Speech Corpus based on Speech-Act Tag." The oriental chapter of COCOSDA (The International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques), 2005. http://hdl.handle.net/2237/15464.

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Books on the topic "Dialogue corpus"

1

Schoenborn, Ulrich. Dialog und Offenbarung: Zur Strategie literarischer Vergewisserung in Krisenzeiten. Lit, 1996.

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Keller, Pascal-Henri. Le dialogue du corps et de l'esprit. Odile Jacob, 2006.

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Keller, Pascal-Henri. Le dialogue du corps et de l'esprit. Odile Jacob, 2006.

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4

Lire les dialogues, mais lesquels et dans quel ordre?: Définitions du corpus et interprétations de Platon. Academia Verlag, 2013.

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Kytö, Merja. Guide to A Corpus of English Dialogues 1560-1760. Uppsala Universitet, 2006.

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6

Wall, Anthony. Ce corps qui parle: Pour une lecture dialogique de Denis Diderot : essai. XYZ éditeur, 2005.

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7

Los dorismos del Corpus Bucolicorum. A.M. Hakkert, 1990.

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8

Lombardo, Luca. Albertino Mussato, Epistole metriche Edizione critica, traduzione e commento. Fondazione Università Ca’ Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-436-3.

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The Metric Epistles of Albertino Mussato (1261-1329) are a collection of 20 compositions in Latin verse (of which, 12 in elegiac couplets, 8 in hexameters, for a total of 1,570 verses) composed between 1309 and 1326 and addressed to different recipients. The list of recipients includes friends of the author and representatives of the Paduan political and intellectual élite of the early 14th century such as the judges Rolando da Piazzola, Giovanni da Vigonza and Paolo da Teolo, the notary Zambono d’Andrea and Marsilio Mainardini; masters of grammar and rhetoric such as the Venetian Giovanni Cas

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Goul, Pauline, and Usher, eds. Early Modern Écologies. Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789462985971.

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Early Modern Écologies is the first collective volume to offer perspectives on the relationship between contemporary ecological thought and early modern French literature. If Descartes spoke of humans as being ‘masters and possessors of Nature’ in the seventeenth century, the writers taken up in this volume arguably demonstrated a more complex and urgent understanding of the human relationship to our shared planet. Opening up a rich archive of literary and non-literary texts produced by Montaigne and his contemporaries, this volume foregrounds not how ecocriticism renews our understanding of a

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Spencer-Hall, Alicia. Medieval Saints and Modern Screens. Amsterdam University Press, 2017. http://dx.doi.org/10.5117/9789462982277.

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This ground-breaking book brings theoretical perspectives from twenty-first century media, film, and cultural studies to medieval hagiography. Medieval Saints and Modern Screens stakes the claim for a provocative new methodological intervention: consideration of hagiography as media. More precisely, hagiography is most productively understood as cinematic media. Medieval mystical episodes are made intelligible to modern audiences through reference to the filmic - the language, form, and lived experience of cinema. Similarly, reference to the realm of the mystical affords a means to express the

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Book chapters on the topic "Dialogue corpus"

1

Altrov, Rene, and Hille Pajupuu. "6. Estonian emotional speech corpus." In Dialogue Studies. John Benjamins Publishing Company, 2013. http://dx.doi.org/10.1075/ds.21.12alt.

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Horák, Alexander, Lucia Gianitsová, Mária Šimková, Martin Šmotlák, and Radovan Garabík. "Slovak National Corpus." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30120-2_12.

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Morchid, Mohamed, Richard Dufour, Usman Niaz, et al. "SuMACC Project’s Corpus." In Text, Speech and Dialogue. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10816-2_4.

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Strauß, Petra-Maria, and Wolfgang Minker. "Multi-Party Dialogue Corpus." In Proactive Spoken Dialogue Interaction in Multi-Party Environments. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-5992-8_3.

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Ohtake, Kiyonori, and Etsuo Mizukami. "NICT Kyoto Dialogue Corpus." In Handbook of Linguistic Annotation. Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_48.

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Pala, Karel, Pavel Rychlý, and Pavel Smrž. "Text Corpus with Errors." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39398-6_13.

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Kutuzov, Andrey, and Maria Kunilovskaya. "Russian Learner Translator Corpus." In Text, Speech and Dialogue. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10816-2_39.

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Marina, B. ondi. "Corpus Linguistics." In The Routledge Handbook of Language and Dialogue. Routledge, 2017. http://dx.doi.org/10.4324/9781315750583-4.

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Čermák, František. "Information, Language, Corpus and Linguistics." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_7.

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Kilgarriff, Adam. "Getting to Know Your Corpus." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32790-2_1.

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Conference papers on the topic "Dialogue corpus"

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Ohtake, Kiyonori, Teruhisa Misu, Chiori Hori, Hideki Kashioka, and Satoshi Nakamura. "Dialogue act annotation for consulting dialogue corpus." In the 3rd International Universal Communication Symposium. ACM Press, 2009. http://dx.doi.org/10.1145/1667780.1667857.

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Yasuhara, Ryu, Masashi Inoue, Ikuya Suga, and Tetsuo Kosaka. "Large-scale multimodal movie dialogue corpus." In ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ACM, 2016. http://dx.doi.org/10.1145/2993148.2998523.

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Ma, Pingshuo, and Chunwen Li. "Building context-related dialogue systems based on Chinese-Script-Dialogue Corpus." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8865554.

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Nakazato, Shu. "Japanese dialogue corpus of multi-level annotation." In the 1st SIGdial workshop. Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117736.1117737.

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Tseng, Shu-Chuan. "Toward a large spontaneous Mandarin dialogue corpus." In the Second SIGdial Workshop. Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118078.1118101.

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Ge, Wendong, and Bo Xu. "Dialogue Management based on Multi-domain Corpus." In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-4648.

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Jurcicek, Filip, Jiri Zahradil, and Libor Jelinek. "A human-human train timetable dialogue corpus." In Interspeech 2005. ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-448.

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Piperski, A. Ch. "RUSSIAN LANGUAGE AND CORPUS DIVERSITY." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-615-627.

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This paper discusses the use of most widely-known Russian corpora, namely Russian National Corpus, ruTenTen, General Internet Corpus of Russian, and Araneum Russicum Maximum, for the theoretical study of Russian language. Based on a sample of papers from 2019, I demonstrate that scholars, especially theoretical linguists, tend to ignore the opportunities provided by a wide range of Web corpora, even though these resources are well-known to the NLP community. I present a selection of case studies to show that data from “non-classical” corpora can be used for studying various linguistic phenomen

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Vydrin, V. F., and J. J. Méric. "CORPUS-DRIVEN BAMBARA SPELLING DICTIONARY." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-1180-1187.

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A model for the development of a corpus-driven spelling dictionary for the Bambara language is described. First, a list of about 4,000 lexemes characterized by spelling variability is extracted from an electronic BambaraFrench dictionary. At the next stage, a script is applied to determine the number of occurrences of each spelling variant in the Bambara Reference Corpus, separately for the entire Corpus (more than 11 million words) and for its disambiguated subcorpus (about 1.5 million words). Statistics on the diversity of sources and authors are also obtained automatically. The statistical

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Kraljevski, Ivan, and Diane Hirschfeld. "Classification of Correction Turns in Multilingual Dialogue Corpus." In Interspeech 2018. ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1348.

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Reports on the topic "Dialogue corpus"

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Shriberg, Elizabeth, Raj Dhillon, Sonali Bhagat, Jeremy Ang, and Hannah Carvey. The ICSI Meeting Recorder Dialog Act (MRDA) Corpus. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada460980.

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