The innovation of web produced a huge of information, evaluates by empowering Internet users to post their assessments, remarks, and audits on the web. Preprocessing helps to understand a user query in the Information Retrieval (IR) system. IR acts as the container to representation, seeking and access information that relates to a user search string. The information is present in natural language by using some words; it’s not structured format, and sometimes that word often ambiguous. One of the major challenges determines in current web search vocabulary mismatch problem during the preprocessing. In an IR system determine a drawback in web search; the search query string is that the relationships between the query expressions and the expanded terms are limited. The query expressions relate to search term fetching information from the IR. The expanded terms by adding those terms that is most similar to the words of the search string. In this manuscript, we mainly focus on behind user’s search string on the web. We identify the best features within this context for term selection in supervised learning based model. In this proposed system the main focus of preprocessing techniques like Tokenization, Stemming, spell check, find dissimilar words and discover the keywords from the user query because provide better results for the user
STRICT: Information retrieval based search term identification for concept location
