@inproceedings{5747a727d7724cda8186d7cadc826849,
title = "A Research on Sentence Similarity for Question Answering System Based on Multi-feature Fusion",
abstract = "If just consider one feature of sentences to calculate sentences similarity, the performance of system is difficult to reach a satisfactory level. This paper presents a method of combining the features of semantic and structural to compute sentences similarity. It first discusses the methods of calculating the semantic similarity of sentences through word embedding and Tongyici Cilin. Next, it discusses the methods of calculating the morphological similarity and order similarity of sentences, and then combines the features through the neutral network to calculate the total similarity of the sentences. We include results from an evaluation of the system's performance and show that a combination of the features works better than any single approach.",
keywords = "Neural network, Semantic similarity, Sentence similarity, Structural similarity, Word embedding",
author = "Haipeng Ruan and Yuan Li and Qinling Wang and Yu Liu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 ; Conference date: 13-10-2016 Through 16-10-2016",
year = "2017",
month = jan,
day = "12",
doi = "10.1109/WI.2016.0085",
language = "English",
series = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "507--510",
booktitle = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016",
address = "United States",
}