Humans live in a dynamic world, where actions bring about consequences, and the facts and properties associated with entities change over time. Without a robust ability to identify events in NL data and temporally situate them, the real 91aboutness92 of the information can be missed. In appreciation of this need, there has recently been a renewed interest in temporal and event-based reasoning for NLP, aimed at addressing challenges in areas such as information extraction, question-answering, and summarization.

This tutorial will begin with an overview of theoretical work on tense, aspect, and event structure in natural language, as well as the fundamentals of temporal reasoning. It will then go on to discuss the annotation of temporal and event expressions in corpora, including the TimeML specification language and other results from the ARDA/NRRC Workshop on Temporal and Event Recognition for Question Answering Systems (TERQAS). The tutorial will examine how to formally distinguish events and their temporal anchoring in documents, and will discuss algorithms for ordering events mentioned in a document relative to each other and for computing closure over an entire discourse of events.

Tutorial attendees can expect to learn about current methodologies and computational resources, the outstanding problems in the area, as well as obtain follow-up pointers to the research literature. Attendees should be familiar with information extraction and the notion of corpus annotation. The course should appeal to those with an interest in leveraging robust semantic analysis for tasks like question-answering, information extraction, and summarization.

James Pustejovsky is Professor of Computer Science at Brandeis University where he is Director of the Laboratory for Linguistics and Com putation. Pustejovsky conducts research in the areas of computational linguistics, lexical semantics, knowledge representation, bioinformatics, and informat ion retrieval and extraction. He was organizer and PI for the ARDA-sponsored research workshop that created the metadata markup language TimeML. He has participated in numerous DARPA and NSF efforts in Knowledge Extractio n and Natural Language Engineering, including the MUC and TIPSTER projects. His publications include numerous books on semantics and corpus processing.

Inderjeet Mani is a Senior Principal Scientist at the MITRE Corporation in McLean, Virginia, and an adjunct faculty in Computational Linguistics at Georgetown University. Mani92s research, funded by MITRE, NSF, DARPA, and others, includes information extraction, automatic summarization, and bioinformatics. Mani helped develop the TIMEX2 annotation scheme for representing aspects of the meaning of temporal expressions in natural languages under the DARPA TIDES research program. He has worked (with Georgetown University) to develop TIMEX2-annotated corpora and taggers for different languages, and has also (with Columbia University) investigated methods for ordering events in news. His publications include two books on automatic summarization.