
Table is a very common presentation scheme, but few papers touch on table extraction in text data mining. This paper focuses on mining tables from large-scale HTML texts. Table filtering, recognition, interpretation, and presentation are discussed. Heuristic rules and cell similarities are employed to identify tables. The F-measure of table recognition is 86.50%. We also propose an algorithm to capture attribute-value relationships among table cells. Finally, more structured data is extracted and presented.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 98 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
