TY - GEN
T1 - Intent Identification for Knowledge Base Question Answering
AU - Dai, Feifei
AU - Feng, Chong
AU - Wang, Zhiqiang
AU - Pei, Yuxia
AU - Huang, Heyan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/5/9
Y1 - 2018/5/9
N2 - 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.
AB - 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.
KW - convolutional neural networks
KW - entity linking system
KW - knowledge base question answering
KW - question intent identification
UR - http://www.scopus.com/inward/record.url?scp=85048359792&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2017.18
DO - 10.1109/TAAI.2017.18
M3 - Conference contribution
AN - SCOPUS:85048359792
T3 - Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
SP - 96
EP - 99
BT - Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
Y2 - 1 December 2017 through 3 December 2017
ER -