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.
Zhou et al. REF used a 2-dimensional CNN to represent a question-answer pair and ranked the representations using a Recurrent Neural Network.
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
Computer Science