Summary of Word Sense Linking: Disambiguating Outside the Sandbox, by Andrei Stefan Bejgu et al.
Word Sense Linking: Disambiguating Outside the Sandbox
by Andrei Stefan Bejgu, Edoardo Barba, Luigi Procopio, Alberte Fernández-Castro, Roberto Navigli
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Word Sense Disambiguation (WSD) is a long-standing task in natural language processing aimed at resolving word meanings in context. Despite recent advances, WSD still lacks practical applications due to its limitations when applied to plain text. This stems from the assumption that all spans to disambiguate are already identified and candidate senses are provided, both of which are challenging requirements. In contrast, Word Sense Linking (WSL) tackles these issues by asking models to identify spans and link them to their most suitable meanings. A transformer-based architecture is proposed for WSL, with thorough evaluations comparing its performance to state-of-the-art WSD systems adapted to WSL. This work aims to facilitate the integration of lexical semantics into real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Word Sense Disambiguation (WSD) is a way for computers to understand words in sentences better. Right now, it’s hard to use WSD in everyday applications because it assumes that we already know which parts of the sentence need to be understood and what possible meanings those words could have. A new task called Word Sense Linking (WSL) tries to solve this problem by asking computers to figure out which parts of a sentence need understanding and then match them with the right meanings. This paper proposes a special way for computers to do WSL, using a powerful language model, and tests how well it works compared to other methods. The goal is to make it easier to use word sense disambiguation in real-life situations. |
Keywords
» Artificial intelligence » Language model » Natural language processing » Semantics » Transformer
