Name
Abstract | ||
|---|---|---|
Extracting bio-entity relations has emerged as an important task due to the ever-growing number of bio-medical documents. In this paper, we present a simple and novel representation for extracting bio-entity relationships. The state-of-theart systems for such tasks rely on word based representations and variations of linguistic driven features. In contrast, we model bio-text by the most basic character based string representation with a family of string kernels. This eliminates time consuming parsing, issue of rare words and domain specific pre-processing. This simple representation makes our approach fast and flexible for any bio-NLP dataset. We demonstrate comparable performance and faster computation time of our approach versus previous state-of-the-art kernel methods. |
Year | Venue | Field |
|---|---|---|
2016 | BioNLP@ACL | Computer science,Theoretical computer science,Artificial intelligence,Natural language processing,Parsing,Kernel method,Machine learning,String representation,Computation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritambhara Singh | 1 | 40 | 6.95 |
| Qi, Yanjun | 2 | 684 | 45.77 |