A Comparative Analysis of Chemical Named Entity Recognition Using Support Vector Machines

Cheminformatics is the synthesis of computer science and chemistry to collect knowledge about chemicals to provide useful information for drug development. Chemical named entity recognition (CHEM-NER) is the crucial first step to extract useful information from chemical publications and patents. In this dissertation, a classification system based on support vector machine (SVM) which uses wrapper based feature subset selection algorithms is proposed for the CHEM-NER task. The SVM classifier for recognizing chemical named entities needs training and evaluation corpora. Three different standard chemical corpora which contain different number of classes have been used to address the binary-class and multi-class classification problems. Wrapper based feature subset selection algorithms such as Forward Selection, Backward Selection and Simplified Forward Search are used in an attempt to find the most relevant subset of features among several features. The features used include several variations of morphological features, lexical features, orthographic features and spaces. The aim of these experiments is to investigate the classification performance using different subsets of features as well as discovering the most relevant corpus among the available corpora for CHEM-NER task. The results show that in general Forward Search algorithm is more successful in selecting the most suitable subset of features for the CHEM-NER task in terms of F-score measure. Keywords: Chemical Named Entity Recognition, Feature Extraction, Wrapper Based Feature Subset Selection, Support Vector Machines, Text Mining.