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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019
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Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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8
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
Liu, Qianchu; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2019
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9
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019
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10
Second-order contexts from lexical substitutes for few-shot learning of word representations ...
Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2019
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11
A Neural Classification Method for Supporting the Creation of BioVerbNet ...
Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019
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12
Enhancing biomedical word embeddings by retrofitting to verb clusters ...
Chiu, B; Baker, Simon; Palmer, M. - : Apollo - University of Cambridge Repository, 2019
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13
Crowdsourcing and Aggregating Nested Markable Annotations
Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019
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A Neural Classification Method for Supporting the Creation of BioVerbNet
Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019. : https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-018-0193-x, 2019. : Journal of Biomedical Semantics, 2019
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Second-order contexts from lexical substitutes for few-shot learning of word representations
Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics, 2019
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
Liu, Qianchu; McCarthy, D; Vulić, I; Korhonen, Anna-Leena. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019
Abstract: In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models. We propose a novel and challenging task, Bilingual Token-level Sense Retrieval (BTSR). It specifically evaluates the accurate alignment of words with the same meaning in cross-lingual non-parallel contexts, currently not evaluated by existing tasks such as Bilingual Contextual Word Similarity and Sentence Retrieval. We show how the proposed BTSR task highlights the merits of different alignment methods. In particular, we find that using context average type-level alignment is effective in transferring monolingual contextualized embeddings cross-lingually especially in non-parallel contexts, and at the same time improves the monolingual space. Furthermore, aligning independently trained models yields better performance than aligning multilingual embeddings with shared vocabulary. ; Peterhouse College Studentship; ERC Consolidator Grant LEXICAL
URL: https://doi.org/10.17863/CAM.44042
https://www.repository.cam.ac.uk/handle/1810/297000
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A neural classification method for supporting the creation of BioVerbNet
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019
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18
Enhancing biomedical word embeddings by retrofitting to verb clusters
Chiu, B; Baker, Simon; Palmer, M. - : Association for Computational Linguistics, 2019. : https://www.aclweb.org/anthology/W19-50, 2019. : BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task, 2019
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19
Bayesian learning for neural dependency parsing
Shareghi, E; Li, Y; Zhu, Y. - : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019
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