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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 Page: 1 2 3 4 5...11 Hits 1 – 20 of 220 1 Multitask Pointer Network for Multi-Representational Parsing Fernández-González, Daniel; Gómez-Rodríguez, Carlos. - : Elsevier, 2022 BASE Show details 2 Joint learning of morphology and syntax with cross-level contextual information flow Can Buglalilar, Burcu; Aleçakır, Hüseyin; Manandhar, Suresh... In: 2022 ; 1 ; 33 (2022) BASE Show details 3 Analyse en dépendances du français avec des plongements contextualisés Grobol, Loïc; Crabbé, Benoît In: 28e Conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03223424 ; 28e Conférence sur le Traitement Automatique des Langues Naturelles, Jun 2021, Lille (virtuel), France (2021) BASE Show details 4 To be or not to be adultlike in syntax: An experimental study of language acquisition and processing in children ... Lassotta, Romy. - : Université de Genève, 2021 BASE Show details 5 IWPT 2021 Shared Task Data and System Outputs Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2021 BASE Show details 6 Training corpus ssj500k 2.3 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021 BASE Show details 7 PLPrepare: A Grammar Checker for Challenging Cases Hoyos, Jacob In: Electronic Theses and Dissertations (2021) BASE Show details 8 To be or not to be adultlike in syntax: An experimental study of language acquisition and processing in children Lassotta, Romy. - : Université de Genève, 2021 BASE Show details 9 Resourceful at Any Size: A Predictive Methodology Using Linguistic Corpus Metrics for Multi-Source Training in Neural Dependency Parsing Gokcen, Ajda. - 2021 BASE Show details 10 Treebank embedding vectors for out-of-domain dependency parsing Wagner, Joachim; Barry, James; Foster, Jennifer In: Wagner, Joachim orcid:0000-0002-8290-3849 , Barry, James orcid:0000-0003-3051-585X and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Treebank embedding vectors for out-of-domain dependency parsing. In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 05-10 Jul 2020, Online (virtual conference). (2020) BASE Show details 11 Bootstrap methods for multi-task dependency parsing in low-resource conditions ; Méthodes d’amorçage pour l’analyse en dépendances de langues peu dotées Lim, Kyungtae. - : HAL CCSD, 2020 In: https://tel.archives-ouvertes.fr/tel-03477961 ; Linguistics. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLE027⟩ (2020) BASE Show details 12 Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri Tanguy, Ludovic; Brunet, Pauline; Ferret, Olivier In: 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02611042 ; 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.5822-5830 (2020) BASE Show details 13 IWPT 2020 Shared Task Data and System Outputs Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2020 BASE Show details 14 On understanding character-level models for representing morphology ... Vania, Clara. - : The University of Edinburgh, 2020 BASE Show details 15 Linguatec Tolosa Treebank ... Miletic, Aleksandra; Bras, Myriam; Esher, Louise. - : Zenodo, 2020 BASE Show details 16 Linguatec Tolosa Treebank ... Miletic, Aleksandra; Bras, Myriam; Esher, Louise. - : Zenodo, 2020 BASE Show details 17 Demographic-Aware Natural Language Processing Garimella, Aparna. - 2020 Abstract: The underlying traits of our demographic group affect and shape our thoughts, and therefore surface in the way we express ourselves and employ language in our day-to-day life. Understanding and analyzing language use in people from different demographic backgrounds help uncover their demographic particularities. Conversely, leveraging these differences could lead to the development of better language representations, thus enabling further demographic-focused refinements in natural language processing (NLP) tasks. In this thesis, I employ methods rooted in computational linguistics to better understand various demographic groups through their language use. The thesis makes two main contributions. First, it provides empirical evidence that words are indeed used differently by different demographic groups in naturally occurring text. Through experiments conducted on large datasets which display usage scenarios for hundreds of frequent words, I show that automatic classification methods can be effective in distinguishing between word usages of different demographic groups. I compare the encoding ability of the utilized features by conducting feature analyses, and shed light on how various attributes contribute to highlighting the differences. Second, the thesis explores whether demographic differences in word usage by different groups can inform the development of more refined approaches to NLP tasks. Specifically, I start by investigating the task of word association prediction. The thesis shows that going beyond the traditional ``one-size-fits-all'' approach, demographic-aware models achieve better performances in predicting word associations for different demographic groups than generic ones. Next, I investigate the impact of demographic information on part-of-speech tagging and syntactic parsing, and the experiments reveal numerous part-of-speech tags and syntactic relations, whose predictions benefit from the prevalence of a specific group in the training data. Finally, I explore demographic-specific humor generation, and develop a humor generation framework to fill-in the blanks to generate funny stories, while taking into account people's demographic backgrounds. ; PHD ; Computer Science & Engineering ; University of Michigan, Horace H. Rackham School of Graduate Studies ; https://deepblue.lib.umich.edu/bitstream/2027.42/155164/1/gaparna_1.pdf Keyword: Computer Science; Demographic-Aware Humor Generation in Mad Libs; Demographic-Aware Natural Language Processing; Demographic-Aware Word Associations; Engineering; Gender-Bias in Part-of-Speech Tagging and Dependency Parsing; Identifying Demographic Differences in Word Usage; Personalization in Language URL: https://hdl.handle.net/2027.42/155164 BASE Hide details 18 On understanding character-level models for representing morphology Vania, Clara. - : The University of Edinburgh, 2020 BASE Show details 19 Self attended stack pointer networks for learning long term dependencies Can, Burcu; Tuç, Salih. - : Association for Computational Linguistics, 2020 BASE Show details 20 Annotation syntaxique automatique de la partie orale du ORFÉO Nasr, Alexis; Dary, Franck; Béchet, Frédéric... In: Langages, N 219, 3, 2020-08-11, pp.87-102 (2020) BASE Show details Page: 1 2 3 4 5...11

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