Abstract / Description of output
Distributional models are derived from cooccurrences in a corpus, where only a small proportion of all possible plausible cooccurrences will be observed. This results in a very sparse vector space, requiring a mechanism for inferring missing knowledge. Most methods face this challenge in ways that render the resulting word representations uninterpretable, with the consequence that semantic composition becomes hard to model. In this paper we explore an alternative which involves explicitly inferring unobserved co-occurrences using the distributional neighbourhood. We show that distributional inference improves sparse word representations on several word similarity benchmarks and demonstrate that our model is competitive with the state-of-the-art for adjective-noun, noun-noun and verb-object compositions while being fully interpretable.
Original language | English |
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Title of host publication | Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing |
Publisher | Association for Computational Linguistics |
Pages | 1691-1702 |
Number of pages | 12 |
DOIs | |
Publication status | Published - 5 Nov 2016 |
Event | 2016 Conference on Empirical Methods in Natural Language Processing - Austin, United States Duration: 1 Nov 2016 → 5 Nov 2016 https://www.aclweb.org/mirror/emnlp2016/ |
Conference
Conference | 2016 Conference on Empirical Methods in Natural Language Processing |
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Abbreviated title | EMNLP 2016 |
Country/Territory | United States |
City | Austin |
Period | 1/11/16 → 5/11/16 |
Internet address |