Date of Award

2013

Degree Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Joan Morrisey

Keywords

Web studies, Information science, Computer science

Rights

CC BY-NC-ND 4.0

Abstract

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. The users can further refine their questions in the query refinement step for the better answers. A novel search strategy was designed to detect the semantic connections between the question and the documents. This answer retrieval 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.

Share

COinS