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XCOPA: A multilingual dataset for causal commonsense reasoning
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Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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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 ...
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Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
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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 ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Second-order contexts from lexical substitutes for few-shot learning of word representations ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet ...
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Enhancing biomedical word embeddings by retrofitting to verb clusters ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet
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Second-order contexts from lexical substitutes for few-shot learning of word representations
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
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A neural classification method for supporting the creation of BioVerbNet
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Abstract:
Abstract Background VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Results Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. Conclusion This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.
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URL: https://doi.org/10.17863/CAM.35554 https://www.repository.cam.ac.uk/handle/1810/288240
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