Automatic condensation of electronic publications by sentence selection
Information Processing and Management: an International Journal - Special issue: summarizing text
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Role of verbs in document analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Inducing German semantic verb classes from purely syntactic subcategorisation information
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Clustering polysemic subcategorization frame distributions semantically
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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 and analyzing judgment opinions
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Which side are you on?: identifying perspectives at the document and sentence levels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Extended lexical-semantic classification of English verbs
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Automatic text categorization based on content analysis with cognitive situation models
Information Sciences: an International Journal
Cognitive intentionality extraction from discourse with pragmatic-tree construction and analysis
Information Sciences: an International Journal
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We consider the classification of German news stories as either focusing on future-directed beliefs and intentions or lacking these. The method proposed in this article requires only a small set of labeled training data. Rather, we introduce German clues for the automatic identification of future-orientation which are used for automatic labeling of Reuters news stories. We describe the development of a high-precision procedure for automatic labeling in a bootstrapping fashion: A first version of the labeling procedure uses the absence of clues for future-directedness as indicator for non-future-directedness and is able to automatically label about one-third of the Reuters news stories with high precision. Then a perceptron is applied to the automatically labeled news stories in order to semi-automatically acquire an additional set of clues for non-future-directedness. The second version of the labeling procedure additionally uses these clues and achieves remarkably improved results in terms of recall; it can even be extended by a guessing step to perform classification with an error of 22.5%. We also investigate another way to increase the recall by using the automatically labeled news stories as training data for statistical classifiers. Three different types of statistical classifiers are applied in order to address the question, which classifier is most suited for the text classification task considered. The best statistical classifier combined with the results of improved automatic labeling is able to recognize the two classes of news stories with an error of 19%.