@inproceedings{4ba0f9cea316432381a3f1342d1e1190,
title = "A P-LSTM neural network for sentiment classification",
abstract = "Neural network models have been demonstrated to be capable of achieving remarkable performance in sentiment classification. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modelling task. In this work, a novel model based on long short-term memory recurrent neural network (LSTM) called P-LSTM is proposed for sentiment classification. In P-LSTM, three-words phrase embedding is used instead of single word embedding as is often done. Besides, P-LSTM introduces the phrase factor mechanism which combines the feature vectors of the phrase embedding layer and the LSTM hidden layer to extract more exact information from the text. The experimental results show that the P-LSTM achieves excellent performance on the sentiment classification tasks.",
keywords = "LSTM, Phrase factor mechanism, Phrase-embedding",
author = "Chi Lu and Heyan Huang and Ping Jian and Dan Wang and Guo, {Yi Di}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 ; Conference date: 23-05-2017 Through 26-05-2017",
year = "2017",
doi = "10.1007/978-3-319-57454-7_41",
language = "English",
isbn = "9783319574530",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "524--533",
editor = "Kyuseok Shim and Jae-Gil Lee and Longbing Cao and Xuemin Lin and Jinho Kim and Yang-Sae Moon",
booktitle = "Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings",
address = "Germany",
}