Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing(2003)
摘要
This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.
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关键词
bootstrapping process,subjective pattern,subjective sentence,extraction pattern,linguistically rich extraction pattern,high precision,high-precision classifier,large training set,unannotated data,subjective expression
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