@inproceedings{a9e0cbf1e09542f9b8075a464941b07c,
title = "Extended dependency-based word embeddings for aspect extraction",
abstract = "Extracting aspects from opinion reviews is an essential task of fine-grained sentiment analysis. In this paper, we introduce outer product of dependency-based word vectors and specialized features as representation of words. With such extended embeddings composed in recurrent neural networks, we make use of advantages of both word embeddings and traditional features. Evaluated on SemEval 2014 task 4 dataset, the proposed method outperform existing recurrent models based methods, achieving a result comparable with the state-of-the-art method. It shows that it is an effective way to achieve better extraction performance by improving word representations.",
keywords = "Aspect extraction, Representation learning, Sentiment analysis, Sequence labelling, Word embeddings",
author = "Xin Wang and Yuanchao Liu and Chengjie Sun and Ming Liu and Xiaolong Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46681-1\_13",
language = "英语",
isbn = "9783319466804",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "104--111",
editor = "Kazushi Ikeda and Minho Lee and Akira Hirose and Seiichi Ozawa and Kenji Doya and Derong Liu",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "德国",
}