A NEW APPROACH FOR TEXT SIMILARITY USING ARTICLES

Conventional approaches to text analysis and information retrieval which measured document similarity by considering all information in texts are relatively inefficiency for processing large text collections in heterogeneous subject areas. Previous researches showed that evidence from passage can improve retrieval results. But it also raised questions about how passage is defined, how they can be ranked efficiently, and what is their proper rule in long structure documents. Moreover, the frequency of "the" with important sentence is efficiently to summarize the text by dexterity way. We previously proposed an approach for extracting sentences which including article "the" by some restrict rules to carry out effectiveness passages. Based on previous approaches, this paper presents a new Passage SIMilarity (P-SIM) measurements between documents based on effectiveness passages after extracting them using article "the". Moreover, our new approach showing that this method is more efficient than traditional methods. Also, Recall and Precision are achieved by 92.6% and 97.5% respectively, depending on extracted passages. Furthermore, Recall and Precision significantly improved by 38.3% and 44.2% over the traditional method. The proposed methods are applied to 3,990 articles from the large tagged corpus.