Text similarity analysis using IR lists

Natural language processing can be seen as a signal processing problem when the characters, syllabi, words, punctuations in a text are considered as signals. In this article, we present a novel approach that detects text similarity in Turkish, based on the similarities of the lists of retrieved documents when the texts are given as queries to web search engines. The similarities between the URLs contained in the items of the returned lists are measured using statistical methods like euclidean, city-block, chebychev, cosine, correlation, spearman and hamming distances. For experimenting, a corpus of 150 news is developed by gathering news in 50 different topics from 3 Turkish newspapers published during a certain time slot. News on the same topic published in different newspapers are considered as similar texts. Statistical methods are applied on the formed newsXterms matrix; and for each news similar news are ranked from the most similar to least similar. If at least one of the top two is the same with the ones marked manully as similar, it is counted as success. Experimental results show that cosines and correlation distances give the best performance with 84% precision.