@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 {``}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{''} {\textless}{--}{\textgreater} {``}scalding{''} suggests that {``}hot{''} {\textless} {``}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.",
}
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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” \textless–\textgreater “scalding” suggests that “hot” \textless “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” \textless–\textgreater “scalding” suggests that “hot” \textless “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.