Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Focused named entity recognition using machine learning
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
OpinionMiner: a novel machine learning system for web opinion mining and extraction
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An iterative reinforcement approach for fine-grained opinion mining
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Granular computing in opinion target extraction
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Opinion target extraction using word-based translation model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Opinion target extraction for short comments
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Opinion target extraction using partially-supervised word alignment model
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
News Comments on the web express readers' attitudes or opinions about an event or object in the corresponding news article. And opinion target extraction from news comments is very important for many useful Web applications. However, many sentences in the comments are irregular and informal, and sometimes the opinion targets are implicit. Thus the task is very challenging and it has not been investigated yet. In this paper, we propose a new approach to uniformly extracting explicit and implicit opinion targets from news comments by using Centering Theory. The approach uses global information in news articles as well as contextual information in adjacent sentences of comments. Our experimental results verify the effectiveness of the proposed approach.