Improving QA processing by semantic reformulation

With the advance in web search engine techniques and the need to deliver the correct answer to the user query is important task in (QA) Query Answer processing. The objective of the QA processing is to deliver exact answer to the user query rather than a set of document containing diverse answer for a given query. Semantic based query reformulation techniques can be used to retrieve the exact answer form huge number of document retrieved from the search engine. In this paper we used TREC-8, TREC-9, and TREC-10 collection as training set. Different types of question and corresponding answer can use from TREC collection. The QA system automatically retrieves the answer from the document retrieved from the search engine. The semantic relation, syntactic tags between the query and answer pair will be checked with help of word net. Finally weight can be assigned to the candidate answer according to the its length, the distance between keyword and the level semantic similarity between extracted query and answer pair. In this paper we propose Q (query) and A (answer) system vary from most other semantic reformulation learning system.