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Abstract
Extracting relational information about times and events referred to in a document has a wide range of applications, from information retrieval to document summarization. While there has been a long history of work on temporal interpretation in computational linguistics, this has been primarily in the terms of formal theories of interpretation. The advent of the TimeML language (and the creation of the TIMEBANK resource) has made this area more accessible to empirical methods in NLP and has standardized the task of temporal interpretation. In this paper I will overview the TimeML language, discuss some of its properties, and review the recent TempEval competition. In addition I present three sets of experiments in which we apply machine learning techniques to problem of determining the temporal relations that hold among the events and times in a text.

Biography
Graham Katz is an assistant professor of computational linguistics at the Linguistics Department of Georgetown University. He got his Ph.D. in Linguistics and Cognitive Science from the University of Rochester and spent a number of years as a researcher and lecturer in Germany, at the University of Tuebingen, Stuttgart and Osnabrueck. Dr. Katz’s research area is computational and theoretical semantics, with a focus on issues in temporal interpretation.