@inbook{c4c0349a5b7c412fa45223e9e5439e92,
title = "A chinese question answering approach integrating count-based and embedding-based features",
abstract = "Document-based Question Answering system, which needs to match semantically the short text pairs, has gradually become an important topic in the fields of natural language processing and information retrieval. Question Answering system based on English corpus has developed rapidly with the utilization of the deep learning technology, whereas an effective Chinese-customized system needs to be paid more attention. Thus, we explore a Question Answering system which is characterized in Chinese for the QA task of NLPCC. In our approach, the ordered sequential information of text and deep matching of semantics of Chinese textual pairs have been captured by our count-based traditional methods and embedding-based neural network. The ensemble strategy has achieved a good performance which is much stronger than the provided baselines.",
keywords = "Chinese text, DBQA, Question answer, Semantic matching",
author = "Benyou Wang and Jiabin Niu and Liqun Ma and Yuhua Zhang and Lipeng Zhang and Jingfei Li and Peng Zhang and Dawei Song",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
month = dec,
day = "1",
doi = "10.1007/978-3-319-50496-4\_88",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "934--941",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}