@inproceedings{5faab79199fc48eea142f3e0ed47c456,
title = "Convolution-based memory network for aspect-based sentiment analysis",
abstract = "Memory networks have shown expressive performance on aspect based sentiment analysis. However, ordinary memory networks only capture word-level information and lack the capacity for modeling complicated expressions which consist of multiple words. Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism. This model sequentially computes the weights of multiple memory units corresponding to multi-words. This model may capture both words and multi-words expressions in sentences for aspect-based sentiment analysis. Experimental results show that the proposed model outperforms the state-of-the-art baselines.",
author = "Chuang Fan and Qinghong Gao and Jiachen Du and Lin Gui and Ruifeng Xu and Wong, \{Kam Fai\}",
note = "Publisher Copyright: {\textcopyright} 2018 ACM.; 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 ; Conference date: 08-07-2018 Through 12-07-2018",
year = "2018",
month = jun,
day = "27",
doi = "10.1145/3209978.3210115",
language = "英语",
series = "41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018",
publisher = "Association for Computing Machinery, Inc",
pages = "1161--1164",
booktitle = "41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018",
}