@inproceedings{koper-schulte-im-walde-2017-improving,
title = "Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses",
author = {K{\"o}per, Maximilian and
Schulte im Walde, Sabine},
editor = "Camacho-Collados, Jose and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1903",
doi = "10.18653/v1/W17-1903",
pages = "24--30",
abstract = "Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings",
}
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<abstract>Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings</abstract>
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%0 Conference Proceedings
%T Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses
%A Köper, Maximilian
%A Schulte im Walde, Sabine
%Y Camacho-Collados, Jose
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F koper-schulte-im-walde-2017-improving
%X Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings
%R 10.18653/v1/W17-1903
%U https://aclanthology.org/W17-1903
%U https://doi.org/10.18653/v1/W17-1903
%P 24-30
Markdown (Informal)
[Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses](https://aclanthology.org/W17-1903) (Köper & Schulte im Walde, SENSE 2017)
ACL