GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives

Proceedings of SemEval-2016, pages 1248–1255, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives Arman Cohan, Kevin Meurer and Nazli Goharian Information Retrieval Lab Department of Computer Science Georgetown University {arman, nazli}@ir.cs.georgetown.edu, kam346@georgetown.edu Abstract Extraction and interpretation of temporal information from clinical text is essential for clinical practitioners and researchers. SemEval 2016 Task 12 (Clinical TempEval) addressed this challenge using the THYME1 corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; TIMEX3 and EVENT attributes; document-time relations; and narrative container relations. Our approach is based on supervised learning (CRF and logistic regression), utilizing various sets of syntactic, lexical and semantic features with addition of manually crafted rules. Our system demonstrated substantial improvements over the baselines in all the tasks. 1 Introduction SemEval-2016 Task 12 (Clinical TempEval) is a direct successor to 2015 Clinical TempEval (Bethard et al., 2015) and the past I2b2 temporal challenge (Sun et al., 2013). Clinical TempEval is designed to address the challenge of understanding clinical timeline in medical narratives and it is based on the THYME corpus (Styler IV et al., 2014) which includes temporal annotations. 1Temporal Histories of Your Medical Event. https://clear.colorado.edu/TemporalWiki/index.php/Main Page 2TimeML is a standard specification language for events and temporal expressions in natural language. http://www.timeml.org/ Researchers have explored ways to extract temporal information from clinical text. Velupillai et al. (2015) developed a pipeline based on ClearTK3 and SVM with lexical features to extract TIMEX3 and EVENT mentions. In I2b2 2012 temporal challenge, all top performing teams used a combination of supervised classification and rule based methods for extracting temporal information and relations (Sun et al., 2013). Besides THYME corpus, there have been other efforts in clinical temporal annotation including works by Roberts et al. (2008), Savova al. (2009) and Galescu and Blaylock (2012).et Previous work has also investigated extracting temporal relations. Examples of these efforts include: classification by SVM (Chambers et al., 2007), Integer Linear Programming (ILP) for temporal ordering (Chambers and Jurafsky, 2008), Markov Logic Networks (Yoshikawa et al., 2009), hierarchical topic modeling (Alfonseca et al., 2012), and SVM with Tree Kernels (Miller et al., 2013). Clinical TempEval 2016 was focused on designing approaches for timeline extraction in the clinical domain. There were 6 different tasks in the TempEval 2016, which are listed in Table 1. Per TimeML specifications (Pustejovsky et al., 2003), we refer to temporal expressions as TIMEX3 and events as EVENT throughout the paper. Attributes of TIMEX3 and EVENTs are outlined according to the THYME annotations (Styler IV et al., 2014). 16 teams participated in TempEval 2016 (Bethard et al., 2016). For extracting temporal information from clinical text, we utilize supervised learning algorithms 1248 Task Description TS TIMEX3 spans ES EVENT spans TA Attributes of TIMEX3 Class ⟨DATE, TIME, DURATION, QUANTIFIER, PREPOSTEXP, SET⟩ EA Attributes of EVENTs Modality ⟨ACTUAL, HYPOTHETICAL, HEDGED, GENERIC⟩ Degree ⟨MOST, LITTLE, N/A⟩ Polarity …

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