Multi-class Relationship Extraction from Biomedical Literature Using Maximum Entropy

Author

Yao, Lin ; Sun, Chengjie ; Wang, Xiaolong ; Wang, Xuan

Author_Institution

Dept. of Comput. Sci., Harbin Inst. of Technol., Shenzhen, China

fYear

2010

fDate

15-17 Oct. 2010

Firstpage

551

Lastpage

554

Abstract

Relation extraction is a challenging task in biomedical text mining due to the complex of sentences in the biomedical literature. In this paper, we address multi-class relationship extraction problem from biomedical literature using Maximum Entropy model with simple word features. The proposed method is applied to extract the protein-protein interactions. Experiments show the method achieves an accuracy of 73.4% in the corpora built based on the HIV-1 Human Protein Interaction Database, which is a promising result compare to previous works.

Keywords

biology computing; data mining; database management systems; maximum entropy methods; proteins; text analysis; HIV-1 human protein interaction database; biomedical literature; biomedical text mining; maximum entropy; multiclass relationship extraction; protein-protein interactions; Bioinformatics; Data mining; Databases; Entropy; Feature extraction; Protein engineering; Proteins; Machine Learning; Maximum Entropy Model; Protein-protein Interaction; Relationship Extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010 Sixth International Conference on

Conference_Location

Darmstadt

Print_ISBN

978-1-4244-8378-5

Electronic_ISBN

978-0-7695-4222-5

Type

conf

DOI

10.1109/IIHMSP.2010.140

Filename

5635576