Name
Abstract | ||
|---|---|---|
Within the scientific literature, tables are commonly used to present factual and statistical information in a compact way, which is easy to digest by readers. The ability to "understand" the structure of tables is key for information extraction in many domains. However, the complexity and variety of presentation layouts and value formats makes it difficult to automatically extract roles and relationships of table cells. In this paper, we present a model that structures tables in a machine readable way and a methodology to automatically disentangle and transform tables into the modelled data structure. The method was tested in the domain of clinical trials: it achieved an F-score of 94.26% for cell function identification and 94.84% for identification of inter-cell relationships. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-41754-7_14 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Table mining,Text mining,Data management,Data modelling,Natural language processing | Data mining,Data structure,Data modeling,Scientific literature,Concept mining,Text mining,Computer science,Information extraction,Natural language processing,Artificial intelligence,Data management | Conference |
Volume | ISSN | Citations |
9612 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikola Milosevic | 1 | 31 | 2.38 |
| Cassie Gregson | 2 | 6 | 1.18 |
| Robert Hernandez | 3 | 1 | 0.35 |
| Goran Nenadic | 4 | 228 | 13.18 |