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Learning subjective nouns using extraction pattern bootstrapping
Open accessAbstract
We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.
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Citations
Cited by
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Zhu, X., & Goldberg, A. B. (2009) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01548-9 · Cited by 1,766
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Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_18 · Cited by 1,464
Learning extraction patterns for subjective expressions
Riloff, E., & Wiebe, J. (2003) · Proceedings of the 2003 conference on Empirical methods in natural language processing - · doi:10.3115/1119355.1119369 · Cited by 1,059
Self-citationClinical information extraction applications: A literature review
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Metadata
- Authors
- Riloff, E., Wiebe, J., & Wilson, T.
- Venue
- Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -
- Published
- 2003
- Volume
- 4
- Pages
- 25-32
- OpenAlex
- W2098136027
- Cited by
- 494
- References
- 0
- FWCI
- 11.29
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Riloff, E., Wiebe, J., & Wilson, T. (2003). Learning subjective nouns using extraction pattern bootstrapping. Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -. https://doi.org/10.3115/1119176.1119180
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