Description:

Gensim - Python Framework for Vector Space Modelling

Gensim is a Python library for Vector Space Modelling with very large corpora. Target audience is the Natural Language Processing (NLP) community.

Features

• all algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM)
• simple interfaces (think java... then think again):
  • easy to plug in your own input corpus/datastream (simple streaming API)
  • easy to extend with other Vector Space algorithms (simple transformation API)
• efficient streaming implementations of popular algorithms, such as online Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections
• can run Latent Semantic Analysis on a cluster of computers (distributed computing)
• extensive documentation and tutorials

Reference example

>>> from gensim import corpora, models, similarities
>>> 
>>> # load corpus iterator from a Matrix Market file on disk
>>> corpus = corpora.MmCorpus('/path/to/corpus.mm')
>>> 
>>> # initialize a transformation (Latent Semantic Indexing with 200 latent dimensions)
>>> lsi = models.LsiModel(corpus, numTopics=200)
>>> 
>>> # convert the same corpus to latent space and index it
>>> index = similarities.MatrixSimilarity(lsi[corpus])
>>> 
>>> # perform similarity query of another vector in LSI space against the whole corpus
>>> sims = index[query]

Changes to previous version:

• improved Latent Semantic Analysis (incremental SVD) performance: factorizing the English Wikipedia (3.1m documents, 400 factors) now takes 14h even in serial mode (i.e., on a single computer)
• several minor optimizations and bug fixes

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Agnostic

Tags: Latent Semantic Analysis, Latent Dirichlet Allocation, Svd, Random Projections, Tfidf

Archive: download here

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.