Categorizing Comparative Sentences
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.
Cite
@article{arxiv.1809.06152,
title = {Categorizing Comparative Sentences},
author = {Alexander Panchenko and Alexander Bondarenko and Mirco Franzek and Matthias Hagen and Chris Biemann},
journal= {arXiv preprint arXiv:1809.06152},
year = {2019}
}
Comments
In Proceedings of the the 6th Workshop on Argument Mining (ArgMining'2019) August 1st, collocated with ACL 2019 in Florence, Italy