NiCT at TREC 2009: Employing Three Models for Entity Ranking Track
Youzheng Wu, Hideki Kashioka · 2009
This paper describes experiments carried out at NiCT for the TREC 2009 Entity Ranking track. Our main study is to develop an effective approach to rank entities via measuring the “similarities ” between supporting snippets of entities and input query. Three models are implemented to this end. 1) The DLM regards entity ranking as a task of calculating the probabilities of generating input query given supporting snippets of entities via language model. 2) The RSVM ranks entities via a supervised Ranking SVM. 3) The CSVM, an unsupervised model, ranks entities according to the probabilities of input query belonging to topics represented by entities and their supporting snippets via SVM classifier. The evaluation shows that the DLM is the best on P@10, while the RSVM outperforms the others on nDCG. 1