UTH-CCB: The Participation of the SemEval 2015 Challenge – Task 14

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 311–314, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics UTH-CCB: The Participation of the SemEval 2015 Challenge – Task 14 Jun Xu, Yaoyun Zhang, Jingqi Wang, Yonghui Wu, Min Jiang, Ergin Soysal, and Hua Xu School of Biomedical Informatics, The University of Texas Health Science Center at Houston Houston, TX, USA {Jun.Xu, Yaoyun.Zhang, Jingqi.Wang, Yonghui.Wu, Min.Jiang, Ergin.Soysal, Hua.Xu}@uth.tmc.edu Abstract This paper describes the system developed by the University of Texas Health Science Center at Houston (UTHealth), for the 2015 SemEval shared task on “Analysis of Clinical Text” (Task 14). We participated in both sub-tasks: Task 1 for “Disorder Identification”, which aims to detect disorder entities and encode them to UMLS (Unified Medial Language System) CUI (Concept Unique Identifier) and Task 2 for Disorder Slot Filling, where the task is to identify normalized value for modifiers of disorders. For Task 1, we developed an ensemble approach that combined machine learning based named entity recognition classifiers with MetaMap, an existing symbolic biomedical NLP system, to recognize disorder entities, and we used a general Vector Space Model-based approach for disorder encoding to UMLS CUIs. To identify modifiers of disorders (Task 2), we developed Support Vector Machines-based classifiers for each type of modifier, by exploring various types of features. Our system was ranked 3rd for Task 1 and 1st for the Task 2 (both 2A and 2B), demonstrating the effectiveness of machine learning-based approaches for extracting clinical entities and their modifiers from clinical narratives. 1 Introduction Natural language processing (NLP) plays a critical role in unlocking important patient information from narrative clinical texts, to support various clinical applications such as decision support systems and translational research. One of the very important tasks for clinical NLP research is to ex- tract clinical concepts such as diseases and treat- ments. Many clinical NLP systems such as MedLEE system (Friedman et al., 1994), MetaMAP system (Aronson and Lang, 2010) and cTAKES system (Savova et al., 2010), have been developed to extract these important clinical concepts from text. A number of shared tasks for clinical concepts extraction have been organized by different entities, including i2b2 (The Center for Informatics for Integrating Biology and the Bedside), ShARe/CLEF eHealth Evaluation Lab, and SemEval (International Workshop on Semantic Evaluation) (Kelly et al., 2014; Pradhan et al., 2014; Suominen et al., 2013; Uzuner et al., 2011). These challenges have greatly promoted clinical NLP research by building benchmark datasets and innovative methods. The 2015 SemEval Shared Task 14, entitled “Analysis of Clinical Text”, is to identify disorders and their modifiers from clinical text, which is an extension of the SemEval-2014 challenge. The 2015 SemEval challenge consists of two subtasks: Task 1 - disorder recognition, where disorder entities need to be detected and normalized to UMLS CUIs, and Task 2 - disorder slot filling, where the normalized value for nine types of modifiers of disorders are to be identified. Task 2 is further divided into two subtasks: 1) Task 2A …

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