The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Mining knowledge from text using information extraction
Abstract
An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.
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Cited by
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,260
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Metadata
- Authors
- Mooney, R. J., & Bunescu, R.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2005-06
- Volume
- 7
- Issue
- 1
- Pages
- 3-10
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W1986386500
- Cited by
- 292
- References
- 67
- FWCI
- 15.12
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
