Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
An automatic method of finding topic boundaries
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Dynamic Programming Algorithm for Linear Text Segmentation
Journal of Intelligent Information Systems
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 subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Hierarchical organization of unstructured consumer reviews
Proceedings of the 20th international conference companion on World wide web
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
CONSENTO: a new framework for opinion based entity search and summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods.