Title :
Genetic Word Sense Disambiguation Algorithm
Author :
Zhang, ChunHui ; Zhou, Yiming ; Martin, Trevor
Author_Institution :
Sch. of Comput. Sci. & Eng., BeiHang Univ., Beijing
Abstract :
A novel unsupervised genetic word sense disambiguation (GWSD) algorithm is proposed in this paper. The algorithm first uses WordNet to determine all possible senses for a set of words, then a genetic algorithm is used to maximize the overall semantic similarity on this set of words. A novel conceptual similarity function combining domain information is also proposed to compute similarity between senses in WordNet. GWSD is tested on two sets of domain terms and obtains good results. A weighted genetic word sense disambiguation (WGWSD) algorithm is then proposed to disambiguate words in a general corpus. Experiments on SemCor are carried out to compare WGWSD with previous work.
Keywords :
genetic algorithms; natural language processing; text analysis; word processing; WordNet; conceptual similarity function; natural language processing; semantic similarity maximization; unsupervised genetic word sense disambiguation algorithm; Application software; Biological cells; Clustering algorithms; Computer science; Evolution (biology); Frequency; Genetic algorithms; Genetic engineering; Information technology; Testing;
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
DOI :
10.1109/IITA.2008.13
