Fast algorithm for assessing semantic similarity of texts. (1st January 2012)

• Record Type: Journal Article
• Authors: Siemiński, Andrzej
• Is Part Of: International journal of intelligent information and database systems. Volume 6:Number 5(2012)
• Page Start: 495
• Page End: 512
• Publication Date: 2012-01-01
• Subjects: text similarity measures -- synsets -- NLP -- WordNet -- two-layer retrieval -- user dividend; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

The paper presents and evaluates an efficient algorithm for measuring semantic similarity of texts. Calculating the level of semantic similarity of texts is a very difficult task and the proposed up to now methods suffer from computational complexity. This substantially limits their application area. The proposed algorithm tries to reduce the problem by merging a computationally efficient statistical approach to text analysis with a semantic component. The semantic properties of text words are extracted from the WordNet lexical database. The approach was tested using WordNets for two languages: English and Polish. The basic properties of this approach are also studied. The paper concludes with an analysis of the performance of the proposed method on a sample database and suggests some possible application areas.