| Challenge: | a long-standing effort in natural language processing has focused on word sense disambiguation, but little has been explored about how word meaning is extended toward new context. |
| Approach: | They propose a framework that partitions a word type into two pseudo-tokens that mark its different senses and infers whether the meaning can be extended to convey the sense denoted by the token. |
| Outcome: | The proposed framework outperforms other models in predicting plausible novel senses for over 7,500 English words. |
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Systematic word meta-sense extension (2023.emnlp-main)
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| Challenge: | Many words in the lexicon are polysemous in that the same word form can express multiple distinct yet related senses. |
| Approach: | They propose a task to extend word meaning to denote new semantic domains that bear regular semantic relations with existing senses. |
| Outcome: | The proposed method improves language models' ability to extend word meaning on multiple benchmarks of figurative language understanding. |
Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)
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| Challenge: | Existing supervised word sense disambiguation systems do not provide enough information about word senses. |
| Approach: | They propose to incorporate synonyms, example phrases or sentences showing usage of word senses and sense gloss of hypernyms into the sense representations. |
| Outcome: | The proposed system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task. |
To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models (2024.emnlp-main)
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| Challenge: | Word Sense Disambiguiation and Word sense Induction are considered independent problems, but they are often neglected in practice. |
| Approach: | They propose an unsupervised task of learning a soft clustering amongwords that defines a set of concepts directly from data. |
| Outcome: | The proposed approach leverages both a local and global cross-lexicon view to induce concepts and also senses in the context of the proposed task. |
Word Sense Linking: Disambiguating Outside the Sandbox (2024.findings-acl)
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| Challenge: | Word Sense Disambiguation (WSD) systems have performed well on several evaluation benchmarks, but it still struggles to find downstream applications. |
| Approach: | They propose a task where systems have to identify which spans to disambiguate and link them to their most suitable meaning. |
| Outcome: | The proposed task performs above the estimated inter-annotator agreement on a set of words . the proposed system is based on 'transformer-based' architectures and iteratively relaxes the assumptions . |
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives (2021.acl-long)
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| Challenge: | Recent Word Sense Disambiguation systems have approached the upper bound of the task on standard evaluation benchmarks. |
| Approach: | They propose to convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation architecture. |
| Outcome: | The proposed approach surpasses state-of-the-art on English and multilingual datasets by large margins. |
Large Scale Substitution-based Word Sense Induction (2022.acl-long)
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| Challenge: | Word forms are ambiguous, and derive meaning from the context in which they appear . word sense induction can be performed over a corpus-derived sense inventory . |
| Approach: | They propose a word-sense induction method based on pre-trained masked language models . they train a static word embeddings algorithm on the sense-tagged corpus . |
| Outcome: | The proposed method outperforms existing senseful embeddings methods on Wikipedia and on an outlier detection dataset. |
WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)
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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu
| Challenge: | Word sense disambiguation (WSD) is a fundamental task in natural language processing. |
| Approach: | They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization. |
| Outcome: | The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings. |
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. |
| Outcome: | The proposed method outperforms previous studies that adapt contextualized embeddings while controlling deviations from the original embeddables. |
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |
Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)
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Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko
| Challenge: | Existing methods of disambiguation of word senses are based on knowledge bases, taxonomies, and other externally built resources. |
| Approach: | They propose a method that takes a pre-trained word embedding model and induces a fully-fledged word sense inventory for 158 languages. |
| Outcome: | The proposed model is based on a pre-trained word embedding model and induces a fully-fledged word sense inventory in 158 languages. |