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Hits 81 – 100 of 191

81
XCOPA: A multilingual dataset for causal commonsense reasoning
Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020
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82
Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
Glavaš, Goran; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, 2020
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83
SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020
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84
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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85
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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86
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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87
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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88
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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89
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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90
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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91
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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92
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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93
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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94
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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95
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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96
Crowdsourcing and Aggregating Nested Markable Annotations
Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019
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97
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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98
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
Abstract: There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word firstorder contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieved superior performance on naturallyoccurring contexts from corpora.
URL: https://doi.org/10.17863/CAM.44054
https://www.repository.cam.ac.uk/handle/1810/297013
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99
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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100
A neural classification method for supporting the creation of BioVerbNet
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019
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