Back to Search Start Over

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Authors :
Thayer Alshaabi
Colin M. Van Oort
Mikaela Irene Fudolig
Michael V. Arnold
Christopher M. Danforth
Peter Sheridan Dodds
Source :
Frontiers in Artificial Intelligence, Vol 4 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

Details

Language :
English
ISSN :
26248212
Volume :
4
Database :
Directory of Open Access Journals
Journal :
Frontiers in Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
edsdoj.0193157409ca429580592287a1c4803c
Document Type :
article
Full Text :
https://doi.org/10.3389/frai.2021.783778