The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Evaluating and optimizing autonomous text classification systems
Open accessAbstract
Text retrieval systems typically produce a ranking of documents and let a user decide how far down that ranking to go.In contrast, programs that filter text streams, software that categorizes documents, agents which alert users, and many other IR systems must make decisions without human input or supervision.It is important to define what constitutes good effectiveness for these autonomous systems, tune the systems to achieve the highest possible effectiveness, and estimate how the effectiveness changes as new data is processed.We show how to do this for binary text classification systems, emphasizing that different goals for the system lead to different optimal behaviors.Optimizing and estimating effectiveness is greatly aided if classifiers that explicitly estimate the probability of class membership are used.
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Cites
THE PROBABILITY RANKING PRINCIPLE IN IR
ROBERTSON, S. E. (1977) · Journal of Documentation · doi:10.1108/eb026647 · Cited by 1,103
Cited by
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026666 · Cited by 2,104
Self-citationA review of the F-measure: its history, properties, criticism, and alternatives
Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236
Active learning for biomedical citation screening
Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835829 · Cited by 123
From classification to quantification in tweet sentiment analysis
Gao, W., & Sebastiani, F. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0327-z · Cited by 75
Automatically Identifying Health Outcome Information in MEDLINE Records
Demner-Fushman, D., Few, B., Hauser, S. E., & Thoma, G. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1911 · Cited by 35
Metadata
- Authors
- Lewis, D. D.
- Venue
- Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95
- Publisher
- ACM Press
- Published
- 1995
- Pages
- 246-254
- OpenAlex
- W2153962014
- Cited by
- 308
- References
- 1
- FWCI
- 4.08
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Lewis, D. D. (1995). Evaluating and optimizing autonomous text classification systems. Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95. https://doi.org/10.1145/215206.215366
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