Knowledge Commons of Institute of Automation,CAS
Text Classification Improved by Integrating BidirectionalLSTM with Two-dimensional Max Pooling | |
Peng Zhou1; Zhenyu Qi1![]() ![]() ![]() ![]() | |
2016 | |
会议名称 | COLING |
会议日期 | 2016 |
会议地点 | 日本 大阪 |
出版地 | 日本 大阪 |
出版者 | ACL |
摘要 | Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Lan-guage Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimen-sions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accu-racy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40643 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Zhenyu Qi |
作者单位 | 1.CASIA 2.Center for Excellence in Brain Science and IntelligenceTechnology |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Peng Zhou,Zhenyu Qi,Suncong Zheng,et al. Text Classification Improved by Integrating BidirectionalLSTM with Two-dimensional Max Pooling[C]. 日本 大阪:ACL,2016. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2016 COLING zhou.pdf(285KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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