Relevance Feedback Document Retrieval using Non-Relevant Documents(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)

概要

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This paper reports a new document retrieval method using non-relevant documents. From a large data set of documents, we need to find documents that relate to human interesting in as few iterations of human testing or checking as possible. In each iteration a comparatively small batch of documents is evaluated for relating to the human interesting. This method is called relevance feedback. The relevance feedback needs a set of relevant and non-relevant documents. However, the initial presented documents which are checked by a user don't always include relevant documents. Accordingly we propose a feedback method using information of non-relevant documents only. We named this method non-relevance feedback. The irrelevance feedback selects a set of documents based on learning result by One-class SVM. Results from experiments show that this method is able to retrieve a relevant document from a set of non-relevant documents effectively.
社団法人電子情報通信学会の論文
2004-11-27