Name
Title | ||
|---|---|---|
Using Recursive Neural Networks to Detect and Classify Drug-Drug Interactions from Biomedical Texts. |
Abstract | ||
|---|---|---|
The purpose of this paper is to explore in detail how a Recursive Neural Network can be applied to classify drug-drug interactions from biomedical texts. The system is based on MV-RNN, a Matrix-Vector Recursive Neural Network, built from the Stanford constituency trees of sentences. Drug-drug interactions are usually described by long sentences with complex structures (such as subordinate clauses, oppositions, and coordinate structures, among others). Our experiments show a low performance that may be probably due to the parser not being able to capture the structural complexity of sentences in the biomedical domain. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-1666 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Computer science,Artificial intelligence,Artificial neural network,Machine learning,Recursion | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Víctor Suárez-Paniagua | 1 | 0 | 2.37 |
| Isabel Segura-Bedmar | 2 | 435 | 30.96 |