@inproceedings{cocos-etal-2018-learning,
title = "Learning Scalar Adjective Intensity from Paraphrases",
author = "Cocos, Anne and
Wharton, Skyler and
Pavlick, Ellie and
Apidianaki, Marianna and
Callison-Burch, Chris",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1202/",
doi = "10.18653/v1/D18-1202",
pages = "1752--1762",
abstract = "Adjectives like {\textquotedblleft}warm{\textquotedblright}, {\textquotedblleft}hot{\textquotedblright}, and {\textquotedblleft}scalding{\textquotedblright} all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair {\textquotedblleft}really hot{\textquotedblright} {\ensuremath{<}}{--}{\ensuremath{>}} {\textquotedblleft}scalding{\textquotedblright} suggests that {\textquotedblleft}hot{\textquotedblright} {\ensuremath{<}} {\textquotedblleft}scalding{\textquotedblright}. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to {\textquotedblleft}yes/no{\textquotedblright} questions."
}
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<abstract>Adjectives like “warm”, “hot”, and “scalding” all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair “really hot” \ensuremath<–\ensuremath> “scalding” suggests that “hot” \ensuremath< “scalding”. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to “yes/no” questions.</abstract>
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%0 Conference Proceedings
%T Learning Scalar Adjective Intensity from Paraphrases
%A Cocos, Anne
%A Wharton, Skyler
%A Pavlick, Ellie
%A Apidianaki, Marianna
%A Callison-Burch, Chris
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F cocos-etal-2018-learning
%X Adjectives like “warm”, “hot”, and “scalding” all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair “really hot” \ensuremath<–\ensuremath> “scalding” suggests that “hot” \ensuremath< “scalding”. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to “yes/no” questions.
%R 10.18653/v1/D18-1202
%U https://aclanthology.org/D18-1202/
%U https://doi.org/10.18653/v1/D18-1202
%P 1752-1762
Markdown (Informal)
[Learning Scalar Adjective Intensity from Paraphrases](https://aclanthology.org/D18-1202/) (Cocos et al., EMNLP 2018)
ACL
- Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki, and Chris Callison-Burch. 2018. Learning Scalar Adjective Intensity from Paraphrases. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1752–1762, Brussels, Belgium. Association for Computational Linguistics.