Name
Abstract | ||
|---|---|---|
This paper describes research methodologies and experimental settings for the task of relation identification and classification between pairs of medical entities, using clinical data. The models that we use represent a combination of lexical and syntactic features, medical semantic information, terms extracted from a vector-space model created using a random projection algorithm, and additional contextual information extracted at sentence-level. The best results are obtained using an SVM classification algorithm with a combination of the above mentioned features, plus a set of additional features that capture the distributional semantic correlation between the concepts and each relation of interest. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21043-3_17 | Canadian Conference on AI |
Keywords | Field | DocType |
distributional semantic correlation,medical entity,medical semantic information,clinical data,best result,svm classification algorithm,extracting relation,relation identification,random projection algorithm,additional contextual information,additional feature | Random projection,Contextual information,Pattern recognition,Computer science,Support vector machine,Semantic information,Correlation,Artificial intelligence,Relation classification,Syntax | Conference |
Volume | ISSN | Citations |
6657.0 | 0302-9743 | 2 |
PageRank | References | Authors |
0.43 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oana Frunza | 1 | 75 | 7.02 |
| Diana Inkpen | 2 | 1059 | 87.92 |