@InProceedings{li-mao-chen:2017:MultiLing2017,
  author    = {Li, Lei  and  Mao, Liyuan  and  Chen, Moye},
  title     = {Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums},
  booktitle = {Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {32--36},
  abstract  = {Multiple grammatical and semantic features are adopted in content linking and
	argument/sentiment labeling for online forums in this paper. There are mainly
	two different methods for content linking. First, we utilize the deep feature
	obtained from Word Embedding Model in deep learning and compute sentence
	similarity. Second, we use multiple traditional features to locate candidate
	linking sentences, and then adopt a voting method to obtain the final result.
	LDA topic modeling is used to mine latent semantic feature and K-means
	clustering is implemented for argument labeling, while features from sentiment
	dictionaries and rule-based sentiment analysis are integrated for sentiment
	labeling. Experimental results have shown that our methods are valid.},
  url       = {http://www.aclweb.org/anthology/W17-1005}
}