Paper: IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation

ACL ID S14-2052
Title IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

In this paper we briefly describe our super- vised machine learning approach for dis- order mention detection system that we submitted as part of our participation in the SemEval-2014 Shared task. The main goal of this task is to build a system that automatically identifies mentions of clini- cal conditions from the clinical texts. The main challenge lies due in the fact that the same mention of concept may be repre- sented in many surface forms. We develop the system based on the supervised ma- chine learning algorithms, namely Condi- tional Random Field and Support Vector Machine. One appealing characteristics of our system is that most of the features for learning are extracted automatically from the given training or test datasets with- out using deep domain specific resources and/or t...