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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 6 7 8 9...55 Hits 81 – 100 of 1.083 81 Syntactic Transformations in Rule-Based Parsing of Support Verb Constructions: Examples from European Portuguese ... Baptista, Jorge; Mamede, Nuno. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020 BASE Show details 82 Linguatec Tolosa Treebank ... Miletic, Aleksandra; Bras, Myriam; Esher, Louise. - : Zenodo, 2020 BASE Show details 83 Linguatec Tolosa Treebank ... Miletic, Aleksandra; Bras, Myriam; Esher, Louise. - : Zenodo, 2020 BASE Show details 84 "Kansen cogoyama": Bamanankan mankankalan kelennin dɔ ; Featural foot in Bambara ; Le « pied caractéristique », une unité phonétique en bambara Vydrin, Valentin In: ISSN: 0167-6164 ; EISSN: 1613-3811 ; Journal of African Languages and Linguistics ; https://halshs.archives-ouvertes.fr/halshs-03195237 ; Journal of African Languages and Linguistics, De Gruyter, 2020, 41 (2), pp.265-300. ⟨10.1515/jall-2020-2012⟩ ; https://www.degruyter.com/journal/key/JALL/html (2020) BASE Show details 85 Syntactic Priming During Sentence Comprehension: Evidence for the Lexical Boost Traxler, Matthew J.; Tooley, Kristen M.; Pickering, Martin J. In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014, Vol. 40, No. 4, pp. 905-918. (2020) BASE Show details 86 Demographic-Aware Natural Language Processing Garimella, Aparna. - 2020 BASE Show details 87 Understanding and generating language with abstract meaning representation Damonte, Marco. - : The University of Edinburgh, 2020 Abstract: Abstract Meaning Representation (AMR) is a semantic representation for natural language that encompasses annotations related to traditional tasks such as Named Entity Recognition (NER), Semantic Role Labeling (SRL), word sense disambiguation (WSD), and Coreference Resolution. AMR represents sentences as graphs, where nodes represent concepts and edges represent semantic relations between them. Sentences are represented as graphs and not trees because nodes can have multiple incoming edges, called reentrancies. This thesis investigates the impact of reentrancies for parsing (from text to AMR) and generation (from AMR to text). For the parsing task, we showed that it is possible to use techniques from tree parsing and adapt them to deal with reentrancies. To better analyze the quality of AMR parsers, we developed a set of fine-grained metrics and found that state-of-the-art parsers predict reentrancies poorly. Hence we provided a classification of linguistic phenomena causing reentrancies, categorized the type of errors parsers do with respect to reentrancies, and proved that correcting these errors can lead to significant improvements. For the generation task, we showed that neural encoders that have access to reentrancies outperform those who do not, demonstrating the importance of reentrancies also for generation. This thesis also discusses the problem of using AMR for languages other than English. Annotating new AMR datasets for other languages is an expensive process and requires defining annotation guidelines for each new language. It is therefore reasonable to ask whether we can share AMR annotations across languages. We provided evidence that AMR datasets for English can be successfully transferred to other languages: we trained parsers for Italian, Spanish, German, and Chinese to investigate the cross-linguality of AMR. We showed cases where translational divergences between languages pose a problem and cases where they do not. In summary, this thesis demonstrates the impact of reentrancies in AMR as well as providing insights on AMR for languages that do not yet have AMR datasets. Keyword: Abstract Meaning Representation; algorithms; AMR; natural language processing; NLP; parsing; reentrancies URL: https://doi.org/10.7488/era/38 https://hdl.handle.net/1842/36731 BASE Hide details 88 On understanding character-level models for representing morphology Vania, Clara. - : The University of Edinburgh, 2020 BASE Show details 89 RuBQ: A Russian Dataset for Question Answering over Wikidata Korablinov, V.; Braslavski, P. In: Lect. Notes Comput. Sci. ; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020) BASE Show details 90 Self attended stack pointer networks for learning long term dependencies Can, Burcu; Tuç, Salih. - : Association for Computational Linguistics, 2020 BASE Show details 91 Neural Models for Integrating Prosody in Spoken Language Understanding Tran, Trang. - 2020 BASE Show details 92 The Role of Information Theory in Gap-Filler Dependencies Kobele, Gregory; He, Linyang; Xiang, Ming In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 93 MG Parsing as a Model of Gradient Acceptability in Syntactic Islands De Santo, Aniello In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 94 The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions Srivastava, Aarohi; Frank, Robert; Widder, Sarah... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 95 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 96 Transfer of L1 processing strategies to the interpretation of sentence-level L2 input: A cross-linguistic comparison on the resolution of relative clause attachment ambiguities Onur Uludağ In: Eurasian Journal of Applied Linguistics, Vol 6, Iss 2, Pp 155-188 (2020) (2020) BASE Show details 97 Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning Marzinotto, Gabriel; Damnati, Geraldine; Béchet, Frédéric In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02298417 ; Interspeech 2019, Sep 2019, Graz, Austria. pp.799-803, ⟨10.21437/Interspeech.2019-2732⟩ (2019) BASE Show details 98 Representation and Parsing of Multiword Expressions ; Representation and Parsing of Multiword Expressions: Current trends Parmentier, Yannick; Waszczuk, Jakub. - : HAL CCSD, 2019. : Language Science Press, 2019 In: https://hal.archives-ouvertes.fr/hal-01537920 ; Yannick Parmentier; Jakub Waszczuk. Germany. 3, Language Science Press, 2019, Phraseology and Multiword Expressions ; http://langsci-press.org (2019) BASE Show details 99 Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study Barry, James; Wagner, Joachim; Foster, Jennifer In: Barry, James orcid:0000-0003-3051-585X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2019) Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study. In: The 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 3 - 5 Nov 2019, Hong Kong, China. ISBN 978-1-950737-78-9 (2019) BASE Show details 100 Cross-Lingual Transfer of Natural Language Processing Systems Rasooli, Mohammad Sadegh. - 2019 BASE Show details Page: 1 2 3 4 5 6 7 8 9...55

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