GATHERING WEB PAGES OF ENTITIES WITH HIGH PRECISION
Keywords:
Precision, Support Vector Machines, Supervised Learning, Query ExpansionAbstract
A search engine like Yahoo looks for entities such as specific people, places, or things on web pages with search queries. Depending on the granularity of query keywords and performance of a search engine, the retrieved web pages may be in very large number having lots of irrelevant web pages and may be also not in proper order. It's infeasible to manually decide the relevance of each web page due to the large number of retrieved web pages. Another challenge is to develop a language independent relevance classification of search results provided by a search engine. To improve the quality of a search engine it is desirable to automatically evaluate the results of a search engine and decide the relevance of retrieved web pages with the user query and the intended entity, the query is all about. A step towards this improvement is to prune irrelevant web pages out by understanding the needs of a user in order to discover knowledge of entities in a particular domain. We propose a novel method to improve the precision of a search engine which is language independent and also free from search engine query logs and user clicks through data (widely used in recent times). We devise language independent novel features to build support vector machine relevance classification model using which we can automatically classify whether a web page retrieved by a search engine is relevant or not to the desired entity.
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