Papers › Word Sense Disambiguation with Transformer Models
Word Sense Disambiguation with Transformer Models
Pierre-Yves Vandenbussche, Tony Scerri, Ron Daniel Jr.
In this paper, we tackle the task of Word Sense Disambiguation (WSD). We present our system submitted to the Word-in-Context Target Sense Verification challenge, part of the SemDeep workshop at IJCAI 2020 (Breit et al., 2020). That challenge asks participants to predict if a specific mention of a word in a text matches a pre-defined sense. Our approach uses pre-trained transformer models such as BERT that are fine-tuned on the task using different architecture strategies. Our model achieves the best accuracy and precision on Subtask 1 – make use of definitions for deciding whether the target word in context corresponds to the given sense or not. We believe the strategies we explored in the context of this challenge can be useful to other Natural Language Processing tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Entity Linking | WiC-TSV | transformers | Task 1 Accuracy: all | 77.8 | #1 of 8 | Archive leaderboard | report |
| Entity Linking | WiC-TSV | transformers | Task 1 Accuracy: domain specific | 81.0 | #1 of 8 | Archive leaderboard | report |
| Entity Linking | WiC-TSV | transformers | Task 1 Accuracy: general purpose | 75.2 | #1 of 8 | Archive leaderboard | report |
| Entity Linking | WiC-TSV | transformers | Task 3 Accuracy: all | 71.9 | #1 of 8 | Archive leaderboard | report |
| Entity Linking | WiC-TSV | transformers | Task 3 Accuracy: domain specific | 65.7 | #1 of 8 | Archive leaderboard | report |
| Entity Linking | WiC-TSV | transformers | Task 3 Accuracy: general purpose | 77.0 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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