Now, It’s Personal : The Need for Personalized Word Sense Disambiguation

Milton King, Paul Cook


Abstract
Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet’s first senses, the predominant senses of our dataset’s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author’s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author’s sense distributions or predominant senses can greatly increase its performance.
Anthology ID:
2021.ranlp-1.79
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
692–700
Language:
URL:
https://aclanthology.org/2021.ranlp-1.79
DOI:
Bibkey:
Cite (ACL):
Milton King and Paul Cook. 2021. Now, It’s Personal : The Need for Personalized Word Sense Disambiguation. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 692–700, Held Online. INCOMA Ltd..
Cite (Informal):
Now, It’s Personal : The Need for Personalized Word Sense Disambiguation (King & Cook, RANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.ranlp-1.79.pdf
Data
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison