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Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning.

, , , and . CoRR, (2015)

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LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation., , , , , and . SDP@COLING, page 181-187. Association for Computational Linguistics, (2022)Determining the Multiword Expression Inventory of a Surprise Language., , and . COLING, page 471-481. ACL, (2016)Is all that Glitters in Machine Translation Quality Estimation really Gold?, , , , , and . COLING, page 3124-3134. ACL, (2016)Target Word Masking for Location Metonymy Resolution., , , and . COLING, page 3696-3707. International Committee on Computational Linguistics, (2020)WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking., , and . COLING, page 5957-5962. International Committee on Computational Linguistics, (2020)KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning., , , , , and . EMNLP (Findings), page 2918-2928. Association for Computational Linguistics, (2021)A Plethora of Methods for Learning English Countability., and . EMNLP, (2003)Topic Intrusion for Automatic Topic Model Evaluation., , and . EMNLP, page 844-849. Association for Computational Linguistics, (2018)Sequence Effects in Crowdsourced Annotations., , and . EMNLP, page 2860-2865. Association for Computational Linguistics, (2017)Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation., , , and . EMNLP, page 2476-2485. Association for Computational Linguistics, (2017)