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BioMed Research International
Volume 2017, Article ID 8565739, 8 pages
Research Article

Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

Correspondence should be addressed to Francisco M. Couto;

Received 10 April 2017; Revised 20 September 2017; Accepted 15 October 2017; Published 9 November 2017

Academic Editor: Hesham H. Ali

Copyright © 2017 Manuel Lobo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO) is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP) system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier. However, the main novelty of IHP is its validation step based on a set of carefully crafted manual rules, such as the negative connotation analysis, that combined with a dictionary can filter incorrectly identified entities, find missed entities, and combine adjacent entities. The performance of IHP was evaluated using the recently published HPO Gold Standardized Corpora (GSC), where the system Bio-LarK CR obtained the best -measure of 0.56. IHP achieved an -measure of 0.65 on the GSC. Due to inconsistencies found in the GSC, an extended version of the GSC was created, adding 881 entities and modifying 4 entities. IHP achieved an -measure of 0.863 on the new GSC.