The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Table extraction using conditional random fields
Abstract
The ability to find tables and extract information from them is a necessary component of data mining, question answering, and other information retrieval tasks. Documents often contain tables in order to communicate densely packed, multi-dimensional information. Tables do this by employing layout patterns to efficiently indicate fields and records in two-dimensional form.Their rich combination of formatting and content present difficulties for traditional language modeling techniques, however. This paper presents the use of conditional random fields (CRFs) for table extraction, and compares them with hidden Markov models (HMMs). Unlike HMMs, CRFs support the use of many rich and overlapping layout and language features, and as a result, they perform significantly better. We show experimental results on plain-text government statistical reports in which tables are located with 92% F1, and their constituent lines are classified into 12 table-related categories with 94% accuracy. We also discuss future work on undirected graphical models for segmenting columns, finding cells, and classifying them as data cells or label cells.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Cited by
Sutton, C., Rohanimanesh, K., & McCallum, A. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015422 · Cited by 284
Self-citationPDF text classification to leverage information extraction from publication reports
Bui, D. D. A., Del Fiol, G., & Jonnalagadda, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.026 · Cited by 52
“The ML baseline could be improved with sequential ML models such as Hidden Markov Model(52) and Conditional Random Field(53).”
MethodWrangling messy CSV files by detecting row and type patterns
van den Burg, G. J. J., Nazábal, A., & Sutton, C. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00646-y · Cited by 42
Inference of development activities from interaction with uninstrumented applications
Bao, L., Xing, Z., Xia, X., Lo, D., & Hassan, A. E. (2018) · Empirical Software Engineering · doi:10.1007/s10664-017-9547-8 · Cited by 25
Metadata
- Authors
- Pinto, D., McCallum, A., Wei, X., & Croft, W. B.
- Venue
- Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
- Publisher
- ACM
- Published
- 2003-07-28
- Pages
- 235-242
- OpenAlex
- W2034797903
- Cited by
- 355
- References
- 1
- FWCI
- 20.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Pinto, D., McCallum, A., Wei, X., & Croft, W. B. (2003). Table extraction using conditional random fields. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. https://doi.org/10.1145/860435.860479
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
