A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

Computational and Mathematical Methods in Medicine
Abdul Wahab MuzaffarUsman Qamar

Abstract

The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be artic...Continue Reading

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Methods Mentioned

BETA
feature extraction
interaction extraction

Software Mentioned

ANNIE
Open NLP Chunker
GENIA
MetaMap API
GATE
MetaMap
General Architecture for Text Engineering ( GATE )
NLM
Tagger
MeTAE

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