Disorder Identification

Natural language processing and text analy- sis methods offer the potential of uncovering hidden associations from large amounts of un- processed texts. The SemEval-2015 Analy- sis of Clinical Text task …...

We implemented an end-to-end system for dis- order identification and slot filling. For iden- tifying spans for both disorders and their at- tributes, we used a linear chain conditional random …...

We developed a system to participate in shared tasks on the analyzing clinical text. Our system approaches are both machine learning-based and rule-based. We applied the machine learning-based approach for …...

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). …...