The answer extraction model has a direct impact on the performance of the Automatic Question Answering System (QA System). In this paper, an answer extraction model based on named entity recognition was presented. It mainly answers specific questions whose answers are related with the named entity. Firstly, it classified the questions according to answer types. And then it identified named entities with suitable types in the fragmented information. Finally, it got the final answer based on scores. The experiments in the paper proved that the model could accurately answer the questions provided by Text REtrieval Conference (TREC). Thus, the proposed model is easy to implement and its performance is good for specific questions.
Fine-Grained Named Entity Recognition in Question Answering with DBpedia
