This is a word embedding resource built by ourselves with PTE which is a semisupervised representation learning tool proposed by [Tang et al., 2015]. This tool could leverage both labeled and unlabeled data to build a large-scale heterogeneous network and use the network to train the word vectors. In our implementation, on one hand, the labeled data is collected from Amazon by [McAuley et al., 2015]. Specifically, we pick 6 domains, i.e., Books, CDs, Clothing, Electronics, Restaurant and Health and each review is automatically assigned with a positive category if its rating score is 4 or 5 and a negative category if its rating score is 1 or 2. On the other hand, the unlabeled data is the data from SemEval-2015 Task [Pontiki et al., 2015]. The vocabulary size is about 1.2 million and the dimensionality of word vector is 300.
[Tang et al., 2015] Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: predictive text embedding through large-scale heterogeneous text networks. In Proceedings of SIGKDD2015, pages 1165–1174, 2015.
[McAuley et al., 2015] Julian J. McAuley, Rahul Pandey, and Jure Leskovec. Inferring networks of substitutable and complementary products. In Proceedings of SIGKDD2015, pages 785–794, 2015.
[Pontiki et al., 2015] Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. Semeval-2015 task 12: Aspect based sentiment analysis. In Proceedings of NAACL-HLT-2015, pages 486–495, 2015.
The word embedding resource is released at https://pan.baidu.com/s/1Z7BxJ2rf0XlFlPfg7dEf4Q.
Owing to some unknown reasons, the orginal url http://alt.qcri.org/tools/discourse-parser/ could not be accessed. Now, you can download the same discourse segmentation tool from the new address https://github.com/jjwangnlp/codra-rst-parser.
python tests_demo.py
python version >= 2.7
Jingjing Wang, Jie Li, Shoushan Li, Yangyang Kang, Min Zhang, Luo Si. Aspect Sentiment Classidication with both Word-level and Clause-level Attention Networks. In Proceeding of IJCAI-2018.