Vector space model: Difference between revisions

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{{Short description|Model for representing text documents}}

'''Vector space model''' or '''term vector model''' is an algebraic model for representing text documents (or more generally, items) as [[vector space|vectors]] such that the distance between vectors represents the relevance between the documents. It is used in [[information filtering]], [[information retrieval]], [[index (search engine)|index]]ing and relevancyrelevance rankings. Its first use was in the [[SMART Information Retrieval System]].<ref>{{cite journal

| last1 = Berry | first1 = Michael W.

| last2 = Drmac | first2 = Zlatko

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===Free open source software===

* [[Apache Lucene]]. Apache Lucene is a high-performance, open source, full-featured text search engine library written entirely in Java.

* [[OpenSearch (software)]], [[Elasticsearch]] and [[Apache Solr|Solr]]: the twothree most well-known search engine programs (many smaller exist) based on Lucene. Others are also available.

* [[Gensim]] is a Python+[[NumPy]] framework for Vector Space modelling. It contains incremental (memory-efficient) algorithms for [[tf–idf|term frequency-inverse document frequency]], [[Latent Semantic Indexing|latent semantic indexing]], [[Locality sensitive hashing#Random projection|random projections]] and [[Latent Dirichlet Allocation|latent Dirichlet allocation]].

* [[Weka (machine learning)|Weka]]. Weka is a popular data mining package for Java including WordVectors and [[Bag-of-words model|Bag Of Words models]].