ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge

By Xiaoqiang Zhou, Baotian Hu, Jiaxin Lin, Yang Xiang, Xiaolong Wang. 2015

23 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

In Information Systems

In Natural Language Processing

Citations in the corpus

Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, background): “Although deep neural networks (DNN) have dominated almost every field of natural language processing, such as sentiment classification (Tang, Qin, and Liu 2015), machine translation (Bahdanau, Cho, and Bengio 2014) and question answering (Zhou et al. 2015), generating high-quality summaries from lon”
SemEval-2016 Task 3: Community Question Answering (2019, background): “In a follow-up work, Zhou et al. (2015a) included long-short term memory (LSTM) units in their convolutional neural network to learn the classiﬁcation sequence for the thread.”