Categorizing Comparative Sentences

Alexander Panchenko, Alexander Bondarenko, Mirco Franzek, Matthias Hagen, Chris Biemann


Abstract
We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., “Python has better NLP libraries than MATLAB” → Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of “better” or “worse”). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.
Anthology ID:
W19-4516
Volume:
Proceedings of the 6th Workshop on Argument Mining
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–145
Language:
URL:
https://www.aclweb.org/anthology/W19-4516
DOI:
10.18653/v1/W19-4516
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-4516.pdf