Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation (L18-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English. |
| Approach: | They release six large-scale sense-annotated datasets in multiple languages to pave the way for supervised multilingual Word Sense Disambiguation. |
| Outcome: | The results show that large-scale sense annotations can be used as training sets for supervised systems. |
Similar Papers
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
Multilingual Word Sense Disambiguation with Unified Sense Representation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing researches on word sense disambiguation focus on English only. |
| Approach: | They propose to build knowledge and supervised based multilingual word sense disambiguation systems on a multilingual lexicon describing the same set of concepts across languages. |
| Outcome: | The proposed model can understand the fine-grained semantics of words under specific contexts. |
Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages . |
| Approach: | They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning. |
| Outcome: | The proposed method outperforms existing methods on multilingual and domain-specific settings. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
Copied to clipboard
| 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. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |
Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)
Copied to clipboard
Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak
| Challenge: | Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context. |
| Approach: | They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations . |
| Outcome: | The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say . |
Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Word Sense Disambiguation rely heavily on manually annotated data, which limits coverage and generalization. |
| Approach: | They propose a framework that leverages large language models as knowledge distillers to build silver-standard WSD corpora by combining generation-based distillation and annotation-based disambiguation. |
| Outcome: | The proposed framework outperforms existing methods on general-domain benchmarks by 50% on the most challenging test set and by 1000 times fewer parameters. |
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)
Copied to clipboard
| Challenge: | LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released. |
| Approach: | They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software. |
| Outcome: | The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released. |
Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)
Copied to clipboard
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. |
MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent approaches to word sense disambiguation use encodings of the sense gloss and context information to improve performance. |
| Approach: | They propose a poly-encoder architecture which uses the sense gloss to improve WSD performance. |
| Outcome: | The proposed approach outperforms the state-of-the-art in word sense disambiguation by 1.9 F1 points and on the PARSEME 1.1 English dataset. |