Leveraging question target word features through semantic relation expansion for answer type classification

Answer type classification is a vital step of question answering systems to detect the most suitable target answer type. Highly accurate identification and classification of an answer type can help identify users' question targets and filter out irrelevant candidate answers to improve system performances. This paper proposes a novel hybrid approach, named as ATICM, for automated answer type identification and classification by utilizing both syntactic and semantic analysis. We firstly propose to integrate four strategies to identify question target features by using dependency relations and rules. Afterwards, we leverage semantic relations to expand the extracted features. Our experiment datasets are publicly available UIUC and TREC10 annotated question datasets. The result shows the ATICM approach achieves an accuracy of 93.9% on the UIUC dataset and 92.8% on the TREC10 dataset. The performance outperforms the state-of-the-art baseline methods, demonstrating its effectiveness in answer type classification.