Word Sense Induction
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|
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 DISAMBIGUATION 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.
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.
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.
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.
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.
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).
Automated WordNet Construction Using Word Embeddings
To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches.
INFORMATION RETRIEVAL MACHINE TRANSLATION WORD EMBEDDINGS WORD SENSE INDUCTION