Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph (C18-1)
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| Challenge: | Existing methods to determine the word sense have fewer word set constraints than unsupervised methods. |
| Approach: | They propose a new word sense disambiguation method that generates the context of an ambiguous word by using similarities between an amorphous word and words in the input document. |
| Outcome: | The proposed method outperforms the baseline method on the SemEval-13 and SemEx15 datasets and has higher performance than the state-of-the-art unsupervised knowledge-based system. |
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| Challenge: | Existing similarity-based systems focus on learning sense embeddings using only the sentence where the word appears, neglecting its global context. |
| Approach: | They propose a contextoriented embedding technique that takes better advantage of both word-level and sense-level global context of an ambiguous word for disambiguation. |
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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. |
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| Challenge: | Prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, but these have proven to be less effective for Chinese. |
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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. |
| Approach: | They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense. |
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GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. |
| Approach: | They propose to integrate gloss knowledge into supervised neural networks for Word Sense Disambiguation (WSD) this paper proposes to fine-tune a pre-trained BERT model and achieve new state-of-the-art results on WSD task. |
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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. |
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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. |
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Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (2021.eacl-main)
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| Challenge: | Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem, but gold data suggests that maximizing the probability of a singular sense is not the most suitable training objective for WSD. |
| Approach: | They propose to use Word Sense Disambiguation (WSD) as a multi-label classification problem in which multiple senses can be assigned to each target word. |
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One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words (2020.lrec-1)
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| Challenge: | Existing word-specific classifiers lack the ability to generalize across words and require limited sense-annotated data for every word. |
| Approach: | They propose to learn a single model that derives sense representations and enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources. |
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Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)
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| Challenge: | Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing. |
| Approach: | They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet . |
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