Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Temporal reasoning for decision support in medicine
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
CU-TMP: temporal relation classification using syntactic and semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
LCC-TE: a hybrid approach to temporal relation identification in news text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
USFD2: Annotating temporal expresions and TLINKs for TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
NCSU: Modeling temporal relations with Markov logic and lexical ontology
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
JU_CSE_TEMP: A first step towards evaluating events, time expressions and temporal relations
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Time-oriented question answering from clinical narratives sing semantic-web techniques
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Verification of temporal scheduling constraints in clinical practice guidelines
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
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Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.