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I'm always astonished how little mention gensim gets, considering that it can basically be used for all the listed tasks, including parsing, if you combine it w
by fnl 9y ago
I'm always astonished how little mention gensim gets, considering that it can basically be used for all the listed tasks, including parsing, if you combine it with your favorite deep learning library (DyNet, anyone?).
- rpedela 9y agogensim is one of the best libraries for word vectors and summarization. For parsing and NER, Stanford CoreNLP works best in my experience.
- fnl 9y agoWell, a model you fine tune to your specific corpus/domain works even (in fact: much) better... And gensim there gives you the tools to build the best possible embeddings. But you do need a use case and an economic reward for the substantial increase in cost than a pre-trained, vanilla, off-the-shelf parser (model) can give you. Yet, if your domain is technical enough (pharma, finance, law, ... - essentially, all but parsing news, blogs, and tweets...) it might be the only way to get a NLP system that really works.