Vsm lsi
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This document compares the Vector Space Model (VSM) and Latent Semantic Indexing (LSI) techniques for information retrieval. VSM represents documents as vectors in a multi-dimensional space, where cosine similarity between vectors indicates document similarity. LSI builds on VSM by extracting concepts from terms and representing documents based on these concepts, allowing matching of documents using different vocabularies. While VSM is simpler, LSI can handle synonymy and polysemy better. Both are commonly used in search engines.
![Vector Space Model
Models documents as a vector in a multi-dimensional space.
Similar documents are closer together, angle between vectors
can be interpretted as similarity of two documents.
Queries are translated into the vector space, and the nearest
documents (point in space, or vector angle) are the desired
documents.
Originated from the SMART Information Retrieval project at
Cornell University. First published paper in 1975 [2].](https://image.slidesharecdn.com/vsmlsi-120502221713-phpapp01/85/Vsm-lsi-4-320.jpg)
![Comparison: VSM vs. LSI
Advantages of LSI
Handles synonymy and polysemy directly
Can match documents using differing vocabularies.
Can even match across different languages, after some
translated documents have been handled[1].
Advantages of VSM
Much simpler, but still performs well
Handles new documents more easily, LSI’s dimension
reduction can cause problems with this.](https://image.slidesharecdn.com/vsmlsi-120502221713-phpapp01/85/Vsm-lsi-10-320.jpg)
Vsm lsi
- 1. Vector Space Model & Lantent Semantic Indexing Ryan Reck November 18, 2008
- 2. 1 Introduction 2 Vector Space Model 3 Lantent Semantic Indexing 4 Applications of VSM & LSI 5 Comparison: VSM vs. LSI 6 Conclusion 7 References
- 3. Introduction What are VSM & LSI? VSM & LSI are techniques from information retrievel for managing documents based on their content.
- 4. Vector Space Model Models documents as a vector in a multi-dimensional space. Similar documents are closer together, angle between vectors can be interpretted as similarity of two documents. Queries are translated into the vector space, and the nearest documents (point in space, or vector angle) are the desired documents. Originated from the SMART Information Retrieval project at Cornell University. First published paper in 1975 [2].
- 5. Vector Space Model Example doc1 =< tf1 , tf2 , tf3 , . . . , tfn > doc2 =< tf1 , tf2 , tf3 , . . . , tfn > sim(doc1 , doc2 ) = cos(θ) = v0 · v1
- 6. Vector Space Model Calcuating Term Weights VSM introduced the Term Frequency - Inverse Document Frequency method of calculating term weights. TF-IDF gives greater weight to less common terms, and less weight to common ones, since rare terms will better distinguish documents than common terms. |D| Wf ,d = tft · log ( |t∈d|
- 7. Lantent Semantic Indexing Built off of Vector Space Model. Extracts concepts from the term-document matrix. Combines corelated dimensions into a single aggrgate dimension. This allows the documents to be indexed by concept instead of simple terms.
- 8. Lantent Semantic Indexing Example Good Example {computer , laptop} − > {1.2 ∗ computer + 0.9 ∗ laptop} Realistic Example {computer , elevator } − > {1.2 ∗ computer + 0.9 ∗ elevator }
- 9. Applications of VSM & LSI VSM, or variations of it, are almost universal. Search Engines Apache Lucene
- 10. Comparison: VSM vs. LSI Advantages of LSI Handles synonymy and polysemy directly Can match documents using differing vocabularies. Can even match across different languages, after some translated documents have been handled[1]. Advantages of VSM Much simpler, but still performs well Handles new documents more easily, LSI’s dimension reduction can cause problems with this.
- 11. Conslusion VSM and LSI are both good ways to index and compare documents. VSM is pretty basic but still gets the job done. LSI provides a more complex system, but it can do a very good job, even under extreme circumstances, like multi-language datasets.
- 12. Refeences Dumais, S. T., Letsche, T. A., Littman, M. L., and Landauer, T. K. Automatic cross-language retrieval using latent semantic indexing. In AAAI Symposium on CrossLanguage Text and Speech Retrieval. American Association for Artificial Intelligence, March 1997. (March 1997). Salton, G., Wong, A., and Yang, C. S. A vector space model for automatic indexing. Commun. ACM 18, 11 (1975), 613–620. Latent semantic indexing, 2008. http://en.wikipedia.com/wiki/Latent semantic indexing. Vector space model, 2008. http://en.wikipedia.com/wiki/Vector space model.