Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection

Laura Ana Maria Oberländer, Roman Klinger


Abstract
Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli (“Is the stimulus category A or B?”), (2) as sequence labeling of tokens (“Which tokens describe the stimulus?”), and (3) as clause classification (“Does this clause contain the emotion stimulus?”). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we analyze whether clause classification or token sequence labeling is better suited for emotion stimulus detection in English. We propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that token sequence labeling is superior on three out of four datasets, in both clause-based and token sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
Anthology ID:
2020.starsem-1.7
Volume:
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
*SEM
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–70
Language:
URL:
https://aclanthology.org/2020.starsem-1.7
DOI:
Bibkey:
Cite (ACL):
Laura Ana Maria Oberländer and Roman Klinger. 2020. Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 58–70, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection (Oberländer & Klinger, *SEM 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.starsem-1.7.pdf
Data
Xia and Ding, 2019