Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation (2020.acl-demos)
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| Challenge: | SyntagRank is a knowledge-based WSD system that exploits syntagmatic information to perform state-of-the-art knowledge-driven WSD in a multilingual setting. |
| Approach: | They propose to exploit syntagmatic information to perform state-of-the-art knowledge-based WSD in a multilingual setting by using a Web interface and a RESTful API. |
| Outcome: | SyntagRank exploits disambiguated pairs of words in SyntagNet to perform state-of-the-art knowledge-based WSD in a multilingual setting. |
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| Challenge: | Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. |
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| Challenge: | Neural Word Sense Disambiguation (WSD) uses pre-existing knowledge, but only close neighbors influence prediction. |
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
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| Challenge: | Recent Word Sense Disambiguation systems have approached the upper bound of the task on standard evaluation benchmarks. |
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Semantic Specialization for Knowledge-based Word Sense Disambiguation (2023.eacl-main)
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| Challenge: | Existing methods for knowledge-based Word Sense Disambiguation (WSD) use only lexical knowledge to adapt contextualized embeddings. |
| Approach: | They propose a semantic specialization where contextualized embeddings are adapted to the WSD task using only lexical knowledge. |
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Multilingual Word Sense Disambiguation with Unified Sense Representation (2022.coling-1)
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| Challenge: | Existing researches on word sense disambiguation focus on English only. |
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Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)
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| Challenge: | Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution. |
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SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc (2025.naacl-long)
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| Challenge: | Recent studies show that language understanding offered by chat-based Large Language Models is limited and far from human-like performance. |
| Approach: | They propose a framework for multilingual Word Sense Disambiguation using group algebra. |
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AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
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| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
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