Word Sense Induction
19 papers with code • 1 benchmarks • 1 datasets
Word sense induction (WSI) is widely known as the “unsupervised version” of WSD. The problem states as: Given a target word (e.g., “cold”) and a collection of sentences (e.g., “I caught a cold”, “The weather is cold”) that use the word, cluster the sentences according to their different senses/meanings. We do not need to know the sense/meaning of each cluster, but sentences inside a cluster should have used the target words with the same sense.
Description from NLP Progress
Benchmarks
These leaderboards are used to track progress in Word Sense Induction
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BRWAC
Most implemented papers
Word Sense Induction with Neural biLM and Symmetric Patterns
asafamr/SymPatternWSI
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EMNLP 2018
An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors.
AutoSense Model for Word Sense Induction
Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word.
UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses
Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users.
An Evaluation Method for Diachronic Word Sense Induction
The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account.
PolyLM: Learning about Polysemy through Language Modeling
AlanAnsell/PolyLM
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EACL 2021
To avoid the "meaning conflation deficiency" of word embeddings, a number of models have aimed to embed individual word senses.
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Lexical substitution, i. e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc.
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups.
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change
Our evaluation is performed across different languages on eight available benchmarks for LSC, and shows that (i) APD outperforms other approaches for GCD; (ii) XL-LEXEME outperforms other contextualized models for WiC, WSI, and GCD, while being comparable to GPT-4; (iii) there is a clear need for improving the modeling of word meanings, as well as focus on how, when, and why these meanings change, rather than solely focusing on the extent of semantic change.
