Statistical character-based syntax similarity measurement for detecting biomedical syntax variations through named entity recognition

Abstract

In this study an approach for detecting biomedical syntax variations through the Named Entity Recognition (NER) called Statistical Character-Based Syntax Similarity (SCSS) is proposed which is used by dictionary-based NER approaches. Named Entity Recognition for biomedical literatures is extraction and recognition of biomedical names. There are different types of NER approaches, that the most common one is dictionary-based approaches. For a given unknown pattern, Dictionary-Based approaches, search through a biomedical dictionary and finds the most common similar patterns to assign their biomedical types to the given unknown pattern. Biomedical literatures include syntax variations, which means two different patterns, refer to the same biomedical named entity. Hence a similarity function should be able to support all of the possible syntax variations. There are three syntax variations namely: (i) character-level, (ii) word-level, and (iii) word order. The SCSS is able to detect all of the mentioned syntax vitiations. This study is evaluated based on two measures: recall and precision which are used to calculate a balanced F-score. Result is satisfied as recall is 92.47% and precision is 96.7%, while the f-test is 94.53%. © 2011 Springer-Verlag.