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Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning
- 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