@inproceedings{14890e40ace34cc4a9937b6ef874367f,
title = "Extracting Chinese explanatory expressions with discrete and neural CRFs",
abstract = "Recent work on opinion mining typically focuses on subtasks such as aspect mining or polarity classification, ignoring the detailed explanatory evidences that account for one certain user opinion. In this paper, we study the extraction of explanatory expressions, by modeling the problem based on conditional random field (CRF). We compare the effectiveness of both discrete and neural features, and further integrate them. We evaluate the models on two datasets from two different domains which have been annotated with ground-truth explanatory expression. Results show that the neural CRF model performs better than the discrete CRF. After a combination of the discrete and neural features, our final CRF mode achieves the top-performing results.",
keywords = "Conditional random field, Explanatory expression extraction, Neural network",
author = "Da Pan and Mengqi Wang and Meishan Zhang and Guohong Fu",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Nature Singapore Pte Ltd.; 3rd International Conference of Pioneer Computer Scientists, Engineers, and Educators, ICPCSEE 2017 ; Conference date: 22-09-2017 Through 24-09-2017",
year = "2017",
doi = "10.1007/978-981-10-6388-6\_1",
language = "英语",
isbn = "9789811063879",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "1--12",
editor = "Qilong Han and Beiji Zou and Xiaoning Peng and Zeguang Lu and Guanglu Sun and Weipeng Jing",
booktitle = "Data Science - 3rd International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Proceedings",
address = "德国",
}