The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A machine learning based approach for table detection on the web
Abstract
Table is a commonly used presentation scheme, especially for describing relational information. However, table understanding remains an open problem. In this paper, we consider the problem of table detection in web documents. Its potential applications include web mining, knowledge management, and web content summarization and delivery to narrow-bandwidth devices. We describe a machine learning based approach to classify each given table entity as either genuine or non-genuine. Various features reflecting the layout as well as content characteristics of tables are studied.In order to facilitate the training and evaluation of our table classifier, we designed a novel web document table ground truthing protocol and used it to build a large table ground truth database. The database consists of 1,393 HTML files collected from hundreds of different web sites and contains 11,477 leaf TABLE elements, out of which 1,740 are genuine tables. Experiments were conducted using the cross validation method and an F-measure of 95.89% was achieved.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cited by
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 707
WebTables: exploring the power of tables on the web
Cafarella, M. J., Halevy, A., Wang, D. Z., Wu, E., & Zhang, Y. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453916 · Cited by 629
A Comprehensive Survey on Automatic Knowledge Graph Construction
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024) · ACM Computing Surveys · doi:10.1145/3618295 · Cited by 382
Wang, Y., Wang, R., Jung, C., & Kim, Y.-S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517469 · Cited by 28
Self-citation
Metadata
- Authors
- Wang, Y., & Hu, J.
- Venue
- Proceedings of the 11th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2002-05-07
- Pages
- 242-250
- OpenAlex
- W1988217119
- Cited by
- 199
- References
- 1
- FWCI
- 7.40
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
