Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection

Comput Math Methods Med. 2015:2015:913489. doi: 10.1155/2015/913489. Epub 2015 Mar 12.

Abstract

Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computer Simulation
  • Information Storage and Retrieval
  • Language
  • Linguistics
  • Machine Learning
  • Medical Informatics / methods*
  • Pattern Recognition, Automated
  • Pharmaceutical Preparations*
  • Terminology as Topic*

Substances

  • Pharmaceutical Preparations