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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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3
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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4
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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5
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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6
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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7
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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14
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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15
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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16
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
Liu, Qianchu; McCarthy, D; Vulić, I. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019
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17
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; Korhonen, Anna-Leena. - : 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
Abstract: Verbs play a fundamental role in many biomedical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes. In this work, we show that by using semantic clusters for verbs, a large lexicon of verb classes derived from biomedical literature, we are able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely available “off-theshelf” retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.
URL: https://www.repository.cam.ac.uk/handle/1810/295397
https://doi.org/10.17863/CAM.42452
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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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20
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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