@inproceedings{panchenko-etal-2019-categorizing,
title = "Categorizing Comparative Sentences",
author = "Panchenko, Alexander and
Bondarenko, Alexander and
Franzek, Mirco and
Hagen, Matthias and
Biemann, Chris",
editor = "Stein, Benno and
Wachsmuth, Henning",
booktitle = "Proceedings of the 6th Workshop on Argument Mining",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4516",
doi = "10.18653/v1/W19-4516",
pages = "136--145",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Categorizing Comparative Sentences
%A Panchenko, Alexander
%A Bondarenko, Alexander
%A Franzek, Mirco
%A Hagen, Matthias
%A Biemann, Chris
%Y Stein, Benno
%Y Wachsmuth, Henning
%S Proceedings of the 6th Workshop on Argument Mining
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F panchenko-etal-2019-categorizing
%X 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.
%R 10.18653/v1/W19-4516
%U https://aclanthology.org/W19-4516
%U https://doi.org/10.18653/v1/W19-4516
%P 136-145
Markdown (Informal)
[Categorizing Comparative Sentences](https://aclanthology.org/W19-4516) (Panchenko et al., ArgMining 2019)
ACL
- Alexander Panchenko, Alexander Bondarenko, Mirco Franzek, Matthias Hagen, and Chris Biemann. 2019. Categorizing Comparative Sentences. In Proceedings of the 6th Workshop on Argument Mining, pages 136–145, Florence, Italy. Association for Computational Linguistics.