Improvement of corpus-based semantic word similarity using vector space model

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2009

Author

Esin, Yunus Emre

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This study presents a new approach for finding semantically similar words from corpora using window based context methods. Previous studies mainly concentrate on either finding new combination of distance-weight measurement methods or proposing new context methods. The main di fference of this new approach is that this study reprocesses the outputs of the existing methods to update the representation of related word vectors used for measuring semantic distance between words, to improve the results further. Moreover, this novel technique provides a solution to the data sparseness of vectors which is a common problem in methods which uses vector space model. The main advantage of this new approach is that it is applicable to many of the existing word similarity methods using the vector space model. The other and the most important advantage of this approach is that it improves the performance of some of these existing word similarity measuring methods.

Subject Keywords

Computer enginnering.

URI

http://etd.lib.metu.edu.tr/upload/12610759/index.pdf
https://hdl.handle.net/11511/18610

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Graduate School of Natural and Applied Sciences, Thesis

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Improvement on Corpus-Based Word Similarity Using Vector-Space Models ESİN, yunus emre; ALAN, özgür; Alpaslan, Ferda Nur (2009-09-16) This paper presents a new approach for finding semantically similar words from large text collection using window based context methods. Previous studies on this problem mainly concentrate on finding new methods which are new combination of distance-weight measurement methods or new context methods. The main difference of our approach is that we focus on reprocessing of existing methods' outputs to update the representation of related_word vectors, which are used for measuring semantic distance between word...
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Citation Formats

Y. E. Esin, “Improvement of corpus-based semantic word similarity using vector space model,” M.S. - Master of Science, Middle East Technical University, 2009.