Name
Abstract | ||
|---|---|---|
In this paper we identify some limitations of contemporary information extraction mechanisms in the context of biomedical literature. We present an extraction mechanism that generates structured representations of textual content. Our extraction mechanism achieves this by extracting compound entities, and relationships between them, occuring in text. A detailed evaluation of the relationship and compound entities extracted is presented. Our results show over 62% average precision across 8 relationship types tested with over 82% average precision for compound entity identification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/WIIAT.2008.295 | Web Intelligence |
Keywords | Field | DocType |
compound entities,compound entity,joint extraction,biomedical literature,extraction mechanism,textual content,contemporary information extraction mechanism,relationship type,compound entity identification,detailed evaluation,average precision,information retrieval,head,data mining,text mining,pattern recognition,information extraction,accuracy,bioinformatics | Data mining,Text mining,Information retrieval,Computer science,Information extraction,Relationship extraction | Conference |
Citations | PageRank | References |
5 | 0.52 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cartic Ramakrishnan | 1 | 655 | 43.01 |
| Pablo N. Mendes | 2 | 1070 | 51.09 |
| Rodrigo A. T. S. da Gama | 3 | 5 | 0.86 |
| Guilherme C. N. Ferreira | 4 | 5 | 0.86 |
| Amit P. Sheth | 5 | 10950 | 1885.56 |