This dissertation addresses semantic search of Web services using natural language processing. We first survey various existing approaches, focusing on the fact that the expensive costs of current semantic annotation frameworks result in limited use of semantic search for large scale applications. We then propose a vector space model based service searching framework to combine traditional frequency weighted term-document matrix and syntactical information extracted from a lexical database and a dependency grammar parser. In particular, instead of using terms as the rows in a term-document matrix, we propose using synsets in WordNet to distinguish different meanings of a word under different contexts as well as clustering different words with the similar meaning. Also based on the characteristics of Web services descriptions, we propose an approach to identifying semantically important terms to adjust the weights. Our experiments show that our approach achieves its goal well. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]