Clustering and OCCC approaches in Document Re-ranking
Chong Teng, Yanxiang He, Donghong Ji, Yixuan Geng, Zhewei Mai, Guimin Lin · 2013
In this paper, we describe our approach for information retrieval for question answering (IR4QA) of NTCIR-8 tasks. For improving information retrieval performance, we focus mostly on the document re-ranking technique, which locates between the first retrieval documents and query expansion. In this paper, we employ two approaches in document re-ranking. One is based on entropy clustering, a kind of unsupervised learning technology. Relevant documents from top initial retrieval result can be automatically clustered same class according to information entropy values. That is a continuation of our previous work. The other is One Class Co-Clustering (OCCC) approach. it aims to detect topical terms, and compute document’s topicality score. The method is simple and performs well. The experiment result shows using the two approaches in Document Reranking, Clustering and OCCC, can improve information retrieval performance.