The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Adaptive information extraction
Abstract
The growing availability of online textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644
Muggleton, S. (1991) · New Generation Computing · doi:10.1007/bf03037089 · Cited by 1,044
Cited by
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 707
Developing X-ray Computed Tomography to non-invasively image 3-D root systems architecture in soil
Mooney, S. J., Pridmore, T. P., Helliwell, J., & Bennett, M. J. (2012) · Plant and Soil · doi:10.1007/s11104-011-1039-9 · Cited by 454
Web data extraction, applications and techniques: A survey
Ferrara, E., De Meo, P., Fiumara, G., & Baumgartner, R. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.07.007 · Cited by 396
Metadata
- Authors
- Turmo, J., Ageno, A., & Català, N.
- Venue
- ACM Computing Surveys
- Published
- 2006-07-25
- Volume
- 38
- Issue
- 2
- Pages
- 4
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2421105961
- Cited by
- 191
- References
- 3
- FWCI
- 16.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
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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.
