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.- Anthology ID:
- W17-1005
- Volume:
- Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- George Giannakopoulos, Elena Lloret, John M. Conroy, Josef Steinberger, Marina Litvak, Peter Rankel, Benoit Favre
- Venue:
- MultiLing
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–36
- Language:
- URL:
- https://aclanthology.org/W17-1005
- DOI:
- 10.18653/v1/W17-1005
- Cite (ACL):
- Lei Li, Liyuan Mao, and Moye Chen. 2017. Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums. In Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres, pages 32–36, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums (Li et al., MultiLing 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-1005.pdf