| Challenge: | Existing methods for word sense induction use a language model to predict probable substitutes for target words. |
| Approach: | They propose to use a language model to predict probable substitutes for target words . they replace the ngram-based language model with a recurrent model to generate strong substitute vectors . |
| Outcome: | The proposed method surpasses the current state-of-the-art on the SemEval 2013 task by a large margin. |
Similar Papers
Multilingual Substitution-based Word Sense Induction (2024.lrec-main)
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| Challenge: | Word Sense Induction is the task of finding senses of an ambiguous word . many approaches to WSI are language-specific and are not easily adaptable to new languages. |
| Approach: | They propose to use multilingual substitution-based WSI methods that generalize to any language supported by the underlying multilingual language model with minimal to no adaptation required. |
| Outcome: | The proposed methods perform on par with monolingual approaches on popular English datasets while being language-specific. |
In the LLM era, Word Sense Induction remains unsolved (2025.findings-acl)
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| Challenge: | Existing word sense induction methods rely on predefined sense inventories of arbitrary granularity and require labor intensive sense-annotated data. |
| Approach: | They propose to evaluate an LLM-based word sense induction method for English . they use data augmentation sources, corpus and lexicon, and semi-supervised scenarios to augment the data. |
| Outcome: | The proposed method surpasses the previous SOTA system on a English set by 3.3%. |
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. |
PolyLM: Learning about Polysemy through Language Modeling (2021.eacl-main)
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| Challenge: | Existing methods to embed word senses have been overtaken by contextualized embeddings . alan ansell and jim koenig present a method which can be applied to downstream tasks . |
| Approach: | They propose a method which formulates learning sense embeddings as a language modeling problem. |
| Outcome: | The proposed method performs better than existing sense embedding methods on WSI tasks . it matches the current state-of-the-art specialized WSi method despite having six times fewer parameters . |
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
| Approach: | They propose to use a large-scale comparative study to compare lexical substitution methods . they compare existing and new methods using word sense induction datasets . |
| Outcome: | The proposed methods improve competitive results by incorporating information about the target word into the models. |
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (D18-1)
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| Challenge: | Event detection (ED) and word sense disambiguation (WSD) are similar tasks, but they require different neural representations. |
| Approach: | They propose a method to transfer the knowledge learned on WSD to ED by matching neural representations learned for the two tasks. |
| Outcome: | The proposed method can be applied to event detection and word sense disambiguation datasets. |
ShadowSense: A Multi-annotated Dataset for Evaluating Word Sense Induction (2024.lrec-main)
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| Challenge: | Existing word sense induction datasets are annotated by multiple annotators whose inter-annotator agreement is key reliability score . |
| Approach: | They propose a dataset that is annotated by multiple annotators with a key reliability score for evaluation of systems automatically inducing word senses. |
| Outcome: | The proposed dataset shows that it is more reliable than existing paradigms for word sense induction evaluation. |
Semantic Frame Induction from a Real-World Corpus (2025.acl-srw)
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| Challenge: | Existing studies on semantic frame induction have demonstrated that pre-trained language models (PLMs) have led to more accurate results. |
| Approach: | They conduct semantic frame induction using the Colossal Clean Crawled Corpus and assess the applicability of existing frame inducing methods to real-world data. |
| Outcome: | The proposed methods outperform existing methods on real-world data and can induce frames corresponding to novel concepts. |
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. |
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. |