@inproceedings{li-etal-2017-word,
    title = "Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums",
    author = "Li, Lei  and
      Mao, Liyuan  and
      Chen, Moye",
    editor = "Giannakopoulos, George  and
      Lloret, Elena  and
      Conroy, John M.  and
      Steinberger, Josef  and
      Litvak, Marina  and
      Rankel, Peter  and
      Favre, Benoit",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-1005/",
    doi = "10.18653/v1/W17-1005",
    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."
}