ENHANCED GRAPH BASED WORD REPRESENTATION FOR BIOMEDICAL NAMED ENTITY RECOGNITION

Abstract

As the biomedical literature continues to expand rapidly, the significance of extracting biomedical-named entities from this extensive body of work is steadily increasing. Bio-NER presents a greater challenge compared to general entity recognition due to the non-standard use of abbreviations, synonyms, homonyms, ambiguities, and the continual creation of new biomedical entity names. These factors combine to create a significant hurdle in the accurate identification and classification of biomedical entities. The underperformance of machine learning models in biomedical text analysis is primarily attributed to the inadequate representation of these texts through manually created features. In addressing this challenge, this study aims to create enhanced word representation methods to improve biomedical named entity recognition and are based on enhanced graph-based word representation techniques, utilizing machine learning approaches: CRF, SVM, and ensemble learning. These methods are assessed using the well-known GENIA corpus. The results show that SVM, CRF and ensemble learning with morphological, orthographic and context features achieves good results with overall F-measure of (54.6%), (81.87%) and (85.64) respectively. In addition, experimental results also show that enhanced graph-based word representation techniques achieve higher performance with overall F-measures (85.62%), (89.69%) and (91.17) respectively. Results show that proposed graph-based word representations significantly improve the overall performance of CRF, SVM, and ensemble learning over traditional feature representation techniques. In general, results show that word representation is a key factor in constructing a suitable recognition method.