Understanding temporal information in natural language text is fundamental for deep language understanding, and key to many advanced natural language processing (NLP) applications, such as question answering, information extraction, document summarization and dialog systems. These techniques can be applied in news, medical, history and other domains.
In this dissertation, we first present our hybrid system to automatically extract temporal information from raw text by extracting events, temporal expressions and identifying temporal relations between entities. Our system had a competitive performance in the temporal evaluation shared task - TempEval 2010. Then we present a metric that we developed for the evaluation of temporal annotation. Our metric has been adopted by the premier temporal evaluation shared task, TempEval 2013, to evaluate participating systems. We also present a question-answering (QA) system that can answer temporal questions with temporal reasoning. Our developed QA system can be used to evaluate temporal information understanding capability. Finally, we describe our contributions in improving the existing temporal resources.
|Advisor:||Allen, James F.|
|Commitee:||Bigham, Jeffery P., Carlson, Greg, Gildea, Daniel, Schubert, Lenhart K.|
|School:||University of Rochester|
|Department:||Hajim School of Engineering and Applied Sciences|
|School Location:||United States -- New York|
|Source:||DAI-B 74/03(E), Dissertation Abstracts International|
|Keywords:||Evaluation, Information extraction, Temporal, Timebank, Timeml, Trios|
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