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http://hdl.handle.net/10397/72412
Title: | Representing verbs with rich contexts : an evaluation on verb similarity | Authors: | Chersoni, E Santus, E Lenci, A Blache, P Huang, CR |
Issue Date: | 2016 | Source: | Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, November 1-5 2016, p. 1967-1972 | Abstract: | Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus. | Publisher: | Association for Computational Linguistics | Rights: | ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License (https://creativecommons.org/licenses/by-nc-sa/3.0/). Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). The following publication Chersoni, E., Santus, E., Lenci, A., Blache, P., & Huang, C. R. (2016). Representing verbs with rich contexts: an evaluation on verb similarit. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1967-1972) is available at https://aclanthology.org/D16-1205 |
Appears in Collections: | Conference Paper |
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