How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection

Hiroshi Kanayama, Ran Iwamoto

Abstract
This paper investigates clause-level sentiment detection in a multilingual scenario. Aiming at a high-precision, fine-grained, configurable, and non-biased system for practical use cases, we have designed a pipeline method that makes the most of syntactic structures based on Universal Dependencies, avoiding machine-learning approaches that may cause obstacles to our purposes. We achieved high precision in sentiment detection for 17 languages and identified the advantages of common syntactic structures as well as issues stemming from structural differences on Universal Dependencies. In addition to reusable tips for handling multilingual syntax, we provide a parallel benchmarking data set for further research.
Anthology ID:
2020.lrec-1.500
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4063–4073
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.500
DOI:
Bibkey:
Cite (ACL):
Hiroshi Kanayama and Ran Iwamoto. 2020. How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4063–4073, Marseille, France. European Language Resources Association.
Cite (Informal):
How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection (Kanayama & Iwamoto, LREC 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2020.lrec-1.500.pdf
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