Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

被引：0

作者：

Huang, Binxuan ^{[1
]}

Carley, Kathleen M. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA

来源：

2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspectspecific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.

引用

页码：1091 / 1096

页数：6

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