| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 106 === In an era of information explosion, to obtain the information efficiently is a very important issue when faced with huge data volume, and the information retrieval system has become one of the most commonly used tools. In the field of relevance feedback, Rocchio’s query expansion is a well-known method. The algorithm generates new query terms by analyzing the frequency of terms which residing in relevance documents and non-relevance documents. However, Rocchio’s method only focuses on term frequency and ignores information between terms. In recent years, the idea of semantic search is getting more and more popular. Therefore, based on the user's original query and search results, our research uses Word2Vec which is a neural network model to analyze the semantic information between the original query and the relevance feedback, and combine the co-occurrence analysis to extract the appropriate query expansion terms. The results of experiments verify that the proposed method is effective in document retrieval.
|
|---|
