Name
Abstract | ||
|---|---|---|
A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.3115/1219840.1219901 | ACL |
Keywords | Field | DocType |
relation extraction | Graph,Computer science,Natural language processing,Artificial intelligence,SIMPLE algorithm,Machine learning,Relationship extraction | Conference |
Volume | Citations | PageRank |
P05-1 | 38 | 1.34 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan McDonald | 1 | 4653 | 245.25 |
| Fernando Pereira | 2 | 17717 | 2124.79 |
| Seth Kulick | 3 | 221 | 29.66 |
| R. Scott Winters | 4 | 58 | 3.14 |
| Yang Jin | 5 | 88 | 5.61 |
| Peter S White | 6 | 125 | 7.36 |