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
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Latest papers with no code
Combining Lexical Substitutes in Neural Word Sense Induction
Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning.
Using Wiktionary as a resource for WSD : the case of French verbs
In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French.
Vector representations of text data in deep learning
For document-level representations we propose Binary Paragraph Vector: a neural network models for learning binary representations of text documents, which can be used for fast document retrieval.
Word Sense Induction using Knowledge Embeddings
By grounding them to knowledge bases they are able to learn multi-word representations and are also interpretable.
Disambiguated skip-gram model
This allows us to control the granularity of representations learned by our model.
How much does a word weigh? Weighting word embeddings for word sense induction
The paper describes our participation in the first shared task on word sense induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018).
Leveraging Lexical Substitutes for Unsupervised Word Sense Induction
Word sense induction is the most prominent unsupervised approach to lexical disambiguation.
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings
Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable.
