Comparison of semantic and single term similarity measures for clustering Turkish documents


Yuecesoy B., Oegueduecue S. G.

6th International Conference on Machine Learning and Applications, Ohio, Amerika Birleşik Devletleri, 13 - 15 Aralık 2007, ss.393-398 identifier identifier

  • Doi Numarası: 10.1109/icmla.2007.52
  • Basıldığı Şehir: Ohio
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.393-398

Özet

With the rapid growth of the World Wide Web (www), it becomes a critical issue to design and organize the vast amounts of on-line documents on the web according to their topic. Even for the search engines it is very important to group similar documents in order to improve their performance when a query is submitted to the system. Clustering is useful for taxonomy design and similarity search of documents on such a domain. Similarity is fundamental to many clustering applications on hypertext. In this paper, we will study how measures of similarity are used to cluster a collection of documents on a web site. Most of the document clustering techniques rely on single term analysis of text, such as vector space model. To better group of related documents we propose a new semantic similarity measure. We compare our measure with Wu-Palmer similarity and cosine similarity. Experimental results show that cosine similarity perform better than the semantic similarities. We demonstrate our results on Turkish documents. This is a first study that considers the semantic similarities between Turkish documents.