| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses . |
| Approach: | They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space. |
| Outcome: | The proposed model generalizes over seen and unseen senses, achieving zero-shot learning. |
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All-words Word Sense Disambiguation Using Concept Embeddings (L18-1)
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| Challenge: | Existing work on all-words word sense disambiguation (all-word WSD) uses word embeddings to identify the senses of words in documents. |
| Approach: | They propose a new concept embedding method to predict target word senses . concept embeds are constructed from concept tag sequences created from previous predictions . |
| Outcome: | The proposed concept embeddings improve Japanese all-words word sense disambiguation task. |
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (2020.acl-main)
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| Challenge: | Neural architectures are the current state of the art in Word Sense Disambiguation (WSD) however, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKBs). |
| Approach: | They propose a neural supervised architecture that embeds Lexical Knowledge Bases and exploits pretrained synset embeddings to predict synsets that are not in the training set. |
| Outcome: | The proposed architecture breaks through the 80% ceiling on the concatenation of all standard all-words English evaluation benchmarks. |
Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing senseannotated corpora lack coverage of many instances in WordNet . however, unambiguous words make up a large portion of WordNet while being poorly covered in existing senseannnotated . |
| Approach: | They propose a method to provide annotations for most unambiguous words in a large corpus by using a dataset. |
| Outcome: | The proposed method improves on the original results on Word Sense Disambiguation (WSD) using pre-trained language models and propagation algorithms. |
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
| Outcome: | The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features. |
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders (2020.acl-main)
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| Challenge: | Existing models for Word Sense Disambiguation are not uniformly distributed on rare or unseen senses. |
| Approach: | They propose a bi-encoder model that embeds the target word with its context and the dictionary definition, or gloss, of each sense. |
| Outcome: | The proposed model outperforms previous state-of-the-art models on English all-words WSD, with a 31.1% error reduction on less frequent senses over prior work. |
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. |
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. |
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. |
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. |
| Outcome: | The proposed method bears closer resemblance to how human annotators disambiguate text and can be extended to exploit structured knowledge from semantic networks. |
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. |