A Unified Feature Representation for Lexical Connotations

Emily Allaway, Kathleen McKeown


Abstract
Ideological attitudes and stance are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker. In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Our analysis shows that it aligns well with human judgments. Additionally, we present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.
Anthology ID:
2021.eacl-main.184
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2145–2163
Language:
URL:
https://aclanthology.org/2021.eacl-main.184
DOI:
10.18653/v1/2021.eacl-main.184
Bibkey:
Cite (ACL):
Emily Allaway and Kathleen McKeown. 2021. A Unified Feature Representation for Lexical Connotations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2145–2163, Online. Association for Computational Linguistics.
Cite (Informal):
A Unified Feature Representation for Lexical Connotations (Allaway & McKeown, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.184.pdf
Data
ConceptNet