Abstract
In this paper, we build a hybrid Web-based metric for computing semantic relatedness between words. The method exploits page counts, titles, snippets and URLs returned by a Web search engine. Our technique uses traditional information retrieval methods and is enhanced by page-count-based similarity scores which are integrated with automatically extracted lexico-synantic patterns from titles, snippets and URLs for all kinds of semantically related words provided by WordNet (synonyms, hypernyms, meronyms, antonyms). A support vector machine is used to solve the arising regression problem of word relatedness and the proposed method is evaluated on standard benchmark datasets. The method achieves an overall correlation of 0.88, which is the highest among other metrics up to date.