Using BERT for Word Sense Disambiguation
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Abstract (arXiv)
Word Sense Disambiguation (WSD), which aims to identify the correct sense of a given polyseme, is a long-standing problem in NLP. In this paper, we propose to use BERT to extract better polyseme representations for WSD and explore several ways of combining BERT and the classifier. We also utilize sense definitions to train a unified classifier for all words, which enables the model to disambiguate unseen polysemes. Experiments show that our model achieves the state-of-the-art results on the standard English All-word WSD evaluation.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2019.
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Citing papers per year: 2021: 6; 2022: 1.
7 papers.
- 2022How to Dissect a Muppet: The Structure of Transformer Embedding Spacescited by 19
- 2021Incorporating Word Sense Disambiguation in Neural Language Modelscited by 6
- 2021Context-gloss Augmentation for Improving Word Sense Disambiguationcited by 2
- 2021Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguationcited by 1
- 2021Automatic Construction of Sememe Knowledge Bases via Dictionariescited by 1
- 2021Homonym Identification using BERT -- Using a Clustering Approachcited by 0
- 2021Tapping BERT for Preposition Sense Disambiguationcited by 0
- 2022How to Dissect a Muppet: The Structure of Transformer Embedding Spaces7 Jun 2022
- 2021Tapping BERT for Preposition Sense Disambiguation27 Nov 2021
- 2021Context-gloss Augmentation for Improving Word Sense Disambiguation14 Oct 2021
- 2021Incorporating Word Sense Disambiguation in Neural Language Models15 Jun 2021
- 2021Automatic Construction of Sememe Knowledge Bases via Dictionaries26 May 2021
- 2021Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation8 Apr 2021
- 2021Homonym Identification using BERT -- Using a Clustering Approach7 Jan 2021