Representations and architectures in neural sentiment analysis for morphologically rich languages: A case study from modern Hebrew

Adam Amram, Anat Ben David, Reut Tsarfaty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs: fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances thereof: token-based and morpheme-based. Our experiments show that the effect of representational choices vary with architectural types. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavor also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89% accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks’ task performance.

Original languageEnglish
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
EditorsEmily M. Bender, Leon Derczynski, Pierre Isabelle
PublisherAssociation for Computational Linguistics (ACL)
Pages2242-2252
Number of pages11
ISBN (Electronic)9781948087506
StatePublished - 2018
Externally publishedYes
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: 20 Aug 201826 Aug 2018

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period20/08/1826/08/18

Bibliographical note

Publisher Copyright:
© 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

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