Abstract

With the promotion of the information needs，the rapid growth of the information resources of “Agriculture，Farmers，Rural Area” （AFR），and the constant improvement of the AFR information infrastructure in rural areas，how to enhance information service to meet the information needs has become an urgent problem.The informatization of AFR is the important part of China's informatization.Question Answering （QA） system can more accurately and automatically extract the answer of the question，which was questioned in natural language，from a wide range of information resources.So，to build a QA system serving AFR will be able to promote the application of AFR information and has a positive significance for famers，researchers and policy makers by applying the QA into the AFR information service.

On the basis of the backgroup and the technology，the paperaims at building the QA system serving AFR.Firstly，the paper elaborates the basic concepts and framework of QA system and research topics both at home and abroad，the research contents and methods，significance and the basic structure of this paper.Secondly，the basic theories of Chinese information processing are summarized，and it is also the basis of the study.Thirdly，the AFR concept clusters which represent the knowledge，FAQ system severing AFR based on the mixed strategy，the classification of AFR question，and answer extraction severing AFR are the key technologies of the QA system severing AFR.Finally，building a QA system severing AFR is described.The main research works of this paper are as follows：

First，the research on the AFR concept cluster based K-Nearest Neighbor （KNN）.This part focuses on the AFR knowledge organization and presents the AFR concept cluster.First of all，the method that extracts the entry and interpretation section from the online “Agriculture Dictionary” and the other method that extract the spoken name using the regular expressions are elaborated.The AFR table is designed.Then，the feature words of entity are extracted，artificial selected and merged from the interpretation section.The feature vector and dimensionality reduction using KL transforms are executed.Finally，experiment shows the method is valid.

Second，FAQ system severing AFR based on the mixed strategy.This part is mainly about research on FAQ search matching method.The similarity of the surface and semantic similarity between the questions and the similarity between the user's question and the answer section of question answer pairs are calculated.Then take a mixed strategy to group the two similarities and form the retrieval of the FAQ severing AFR.Finally，the effectiveness of the method is verified by experiments.

Third，the AFR question classification system and method.This paper designs the questionclassification of automatic QA system severing AFR，referring to the classification system of open domain and the AFR domain knowledge.We consider Wh-word，the AFR concept cluster and HowNet sememes as classification features，calculate characteristic value by the information entropy and design the algorithm of a template-based coarse classification and classification based SVM.The experiments show that the feature vector and classification method in this article can effectively meet the demand.

Fourth，the answer extraction of QA system severing AFR.According to different question category and answer source，the paper proposed different method.The method AFR knowledge-based is for factual questions.The method using template cues words of reason is for question of reason.For the “how” question，the method based automatic summarization extraction is proposed.These algorithms are also validated by experiment.

Fifth，the construction and realization of QA system severing AFR.The part describes the network environment，the server-side technologies，the related technologies applied in the system and the results of the system.

Sixth，we draw up the contribution of the research，and we indicate the shortcomings of the research and discuss the future work.

Keywords：Question Answering （QA） Serving “Agriculture，Farmers，Rural Area” （AFR）；AFR concept cluster；Frequently-Asked Question （FAQ） of AFR；AFR question classification；answer extraction