Parameter learning for multi-factors of entity answer extracting

Abstract

Entity extraction involves multi-factors, and the different factor has an impact on the answer in varying degrees, this paper presents a machine learning approach to parameter learning for entity answer. Firstly, in view of characteristics of the Question Answering System (QA), we define three elements of the text score, passage score and entity score which influenced the answer extraction, also give the relevant computational method about them. Then collect 400 entity answers of product, person, and organization according to TREC2009 entity task requirements. With the help of search engines, retrieve related pages and calculate the score of the various factors related to the answer respectively. Thereafter compute the score of entity answers according to a linear combination of the various factors. Define an initial score to extract the entity answer and get a sorted list of answers. Finally, mark these entities answer to obtain the correct marked answers corpus, then build parameter learning model by the EM algorithm iterate gradually to find the optimal answer weight of different factors that influenced the answer extraction. We carried on the experiment in the TREC2009 entity task; it shows very good results for this method. The accuracy of entity answer has achieved 88.93%.