https://github.com/vibhoothi/vsm

Vector Space Model Calculation using NLTK
https://github.com/vibhoothi/vsm

information-retrieval vector-space-model

Last synced: over 1 year ago
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Vector Space Model Calculation using NLTK

• Host: GitHub
• URL: https://github.com/vibhoothi/vsm
• Owner: vibhoothi
• Created: 2019-05-01T20:02:52.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-05-02T09:46:18.000Z (about 7 years ago)
• Last Synced: 2025-01-25T15:13:03.080Z (over 1 year ago)
• Topics: information-retrieval, vector-space-model
• Language: Python
• Size: 27.3 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Vector Space Model

## What are different types of models in Information Retrival 

There are two types of models

* Boolean Retrival 

* Vector Space Model

## Disadvantages of Boolean Retrival Model 

  * Similarity function is boolean

    * Exact-match only, no partial matches

    * Retrieved documents not ranked

  * All terms are equally important

    * Boolean operator usage has much more influence than a critical word

  * Query language is expressive but complicated

## What is Vector Space Model 

* In Vector Space Model both Documents and queries are vectors each w(i,j) is a weight for term j in document i

* "bag-of-words representation"

* Similarity of a document vector to a query vector = cosine of the angle between them

* Cosine is a normalized dot product

* Documents ranked by decreasing cosine value

* Formula is ![](https://i.imgur.com/wdlDQQd.png)

 * sim(d,q) = 1 when d = q

 * sim(d,q) = 0 when d and q share no terms