Word Sense Induction Edit Task
Natural Language Processing • Word Sense Disambiguation
About
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
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
Datasets
Greatest papers with code
Improved Word Representation Learning with Sememes
The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately.
COMMON SENSE REASONING LANGUAGE MODELLING MACHINE TRANSLATION SENTIMENT ANALYSIS WORD EMBEDDINGS WORD SENSE INDUCTION
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words.
Towards better substitution-based word sense induction
• asafamr/bertwsi
• 
Word sense induction (WSI) is the task of unsupervised clustering of word usages within a sentence to distinguish senses.
Ranked #1 on
Word Sense Induction
on SemEval 2010 WSI
Watset: Automatic Induction of Synsets from a Graph of Synonyms
This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings.
Word Sense Induction with Neural biLM and Symmetric Patterns
EMNLP 2018
• asafamr/SymPatternWSI
•
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.
Ranked #3 on
Word Sense Induction
on SemEval 2013
A Simple Approach to Learn Polysemous Word Embeddings
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
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.
Ranked #2 on
Word Sense Induction
on SemEval 2010 WSI
Russian word sense induction by clustering averaged word embeddings
The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018).