@inproceedings{09618e6961e24e309fd01ffcde421993,
title = "ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge",
abstract = "In this paper, we present a comment labeling system based on a deep learning strategy. We treat the answer selection task as a sequence labeling problem and propose recurrent convolution neural networks to recognize good comments. In the recurrent architecture of our system, our approach uses 2-dimensional convolutional neural networks to learn the distributed representation for question-comment pair, and assigns the labels to the comment sequence with a recurrent neural network over CNN. Compared with the conditional random fields based method, our approach performs better performance on Macro-F1 (53.82\%), and achieves the highest accuracy (73.18\%), F1-value (79.76\%) on predicting the Good class in this answer selection challenge.",
author = "Xiaoqiang Zhou and Baotian Hu and Jiaxin Lin and Yang Xiang and Xiaolong Wang",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics; 9th International Workshop on Semantic Evaluation, SemEval 2015 co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 ; Conference date: 04-06-2015 Through 05-06-2015",
year = "2015",
doi = "10.18653/v1/s15-2065",
language = "英语",
series = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "210--214",
editor = "Preslav Nakov and Torsten Zesch and Daniel Cer and David Jurgens",
booktitle = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics",
address = "澳大利亚",
}