tca19/dict2vec
Dict2vec is a framework to learn word embeddings using lexical dictionaries.
This tool helps researchers and natural language processing practitioners create numerical representations of words, known as word embeddings. It takes large text datasets, like Wikipedia articles, and dictionary definitions as input. The output is a set of word vectors that capture the semantic meaning and relationships between words, useful for various language understanding tasks.
115 stars. No commits in the last 6 months.
Use this if you need to train custom word embeddings from your specific text corpora and dictionary definitions, or if you want to evaluate the performance of different word embedding models.
Not ideal if you're looking for an off-the-shelf solution for general text analysis and don't require training custom word representations or detailed evaluation.
Stars
115
Forks
29
Language
Python
License
GPL-3.0
Category
Last pushed
Jan 08, 2021
Commits (30d)
0
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