The nature of statistical learning theory
The nature of statistical learning theory
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Computational Linguistics - Summarization
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A systematic comparison of various statistical alignment models
Computational Linguistics
An introduction to boosting and leveraging
Advanced lectures on machine learning
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The Alignment Template Approach to Statistical Machine Translation
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
Practical Statistics for Medical Research
Practical Statistics for Medical Research
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Active learning for e-rulemaking: public comment categorization
dg.o '08 Proceedings of the 2008 international conference on Digital government research
A study in rule-specific issue categorization for e-rulemaking
dg.o '08 Proceedings of the 2008 international conference on Digital government research
| Hi-index | 0.00 |
Given a large collection of documents, we often need to extract various aspects of information that may be integrated to form a coherent overall picture. Especially for subjective documents addressing a single topic, traditional summarization techniques are limited in differentiating and clustering similar information. We apply multiple classifications to handle diverse aspects, including subtopic identification, keyword extraction, argument structure analysis, and opinion classification, in order to provide a summarized overview of the collection, complete with distributional information. From this overall summary, system users can effectively obtain more fine-grained information. Our methods for individual modules significantly outperform the baseline and achieve human-level agreement.