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sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo.
Features
- Query vectors for multi-word phrases based on part-of-speech tags and entity labels
- spaCy pipeline component and extension attributes
- Fully serializable so you can easily ship your sense2vec vectors with your spaCy model packages
- Optional caching of nearest neighbors for super fast "most similar" queries
- Train your own vectors using a pretrained spaCy model, raw text and GloVe or Word2Vec via fastText (details)
- Prodigy annotation recipes for evaluating models, creating lists of similar multi-word phrases and converting them to match patterns, e.g. for rule-based NER or to bootstrap NER annotation
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Categories
Machine LearningLicense
MIT LicenseFollow sense2vec
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