danlou/LMMS
Language Modelling Makes Sense - WSD (and more) with Contextual Embeddings
This project helps natural language processing practitioners create more precise word sense embeddings. It takes raw text corpora with word sense annotations and transforms them into vector representations that capture the specific meaning of a word in context, rather than just its general form. Linguists, computational semanticists, or anyone working on advanced text understanding would find this useful for tasks like disambiguating word meanings.
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Use this if you need to create sophisticated vector representations of words that account for their different meanings based on context, for tasks like Word Sense Disambiguation (WSD).
Not ideal if you just need basic word embeddings or are not working with word sense annotations and the complexities of contextual meaning.
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Python
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Last pushed
Jun 12, 2023
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