Intent Identification for Knowledge Base Question Answering
Feifei Dai, Chong Feng, Zhiqiang Wang, Yuxia Pei, Heyan Huang · 2017
With the rapid growth of data, knowledge base question answering (KBQA) is becoming more and more important. However, most existing methods of KBQA take every word in the question into account, leading to serious semantic confusion of the question and low efficiency of the question answering (QA) System. Therefore, we proposed an intent identification method to wipe off irrelevant words to decrease the semantic influence. By locating, expanding and disambiguating the subject and its attributes of questions, we not only obviously decrease the time cost of KBQA but also greatly reduce the amount of data processing and search space. Furthermore, by incorporating Convolutional Neural Networks (CNNs) to model questions and answer candidates, the top ranking candidates can be easily identified as answers. Experiments on an well-known track of NLPCC 2016 dataset show that the average F1 score is 75.26%, which is much higher than previous methods.