@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums
%A Li, Lei
%A Mao, Liyuan
%A Chen, Moye
%Y Giannakopoulos, George
%Y Lloret, Elena
%Y Conroy, John M.
%Y Steinberger, Josef
%Y Litvak, Marina
%Y Rankel, Peter
%Y Favre, Benoit
%S Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F li-etal-2017-word
%X 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.
%R 10.18653/v1/W17-1005
%U https://aclanthology.org/W17-1005
%U https://doi.org/10.18653/v1/W17-1005
%P 32-36
Markdown (Informal)
[Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums](https://aclanthology.org/W17-1005) (Li et al., MultiLing 2017)
ACL