C. Suba
Retrieval of information systems such as web interfaces services is a great technology in the web search services. In this paper, we present different approaches of information retrieval using document vector representation. The goal of information retrieval (IR) is to provide users with those documents that will satisfy their information need. Retrieval models can attempt to describe the human Process, such as the information need for interaction. Retrieval of information process has been a prominent and ongoing research in the field of natural language processing. Information Retrieval (IR) is searching for document or information in documents. Document can be text or multimedia and may reside on the web. The vector space mode is one of the classical and widely applied retrieval models to evaluate relevance of web page and efficient search for best documents. The retrieval of information consists of computing the Modified Cosine Coefficient Similarity Measure (MCCSM). To improve the quality of the search result returned by the internet which makes users have to look through a huge amount of links for the real answers, we utilized the high quality links Google produces and the Information Retrieval technology to implement a Question Answering (QA) system. This system analyzes and downloads the text contents from the relevant web pages Google searches based on the users’ questions to build a dynamic knowledge collection; retrieves the relevant passages from the collection and sends the ranked passages back. We utilized the high quality links Google produces and the Information Retrieval technology to implement a Question Answering (QA) system. In this paper, we present retrieval of document also involves the TF-IDF algorithm and Vector Space Model for the document indexing. We have modified the original Cosine Coefficient Similarity Measurement to rank the candidate answers.
TF-IDF; Vector Space Model, Cosine Similarities, Term-Document, Term-Query Matrices, Dot Products.