05151 Summary - Annotating, Extracting and Reasoning about Time and Events

Newspaper articles and other natural-language texts describe actions, events, and states of affairs. A crucial first step toward the automatic extraction of information from these texts—for use in such applications as automatic question answering or summarization—is the capacity to identify what events are being described and to make explicit when these events occurred and which temporal relations hold among them. There has recently been a renewed interest in making use of this kind of temporal and event-based information, with a wide variety of proposals and applications having been presented at recent conferences and workshops. [4, 3, 5, 6]. The central goal of the seminar was to consolidate the insights that have been made in recent years and to identify and address issues concerning annotation, temporal reasoning and event identification that remain unresolved. Much of the temporal information conveyed in a natural language text is left implicit. Significant recent work has focused on developing schema for making this information explicit, typically via annotation. An important result of contemporary research has been the adoption of a de facto standard for time and event annotation: TimeML [5, 6, 7]. This XML-based markup language is specifically designed for annotating texts with tags that make explicit the temporal and event-based information conveyed by the text and has been adopted by a number of researchers in this domain. Much of our seminar was concerned with issues specific to this annotation scheme. There are three basic types of tags used by the TimeML language: TIMEX tags are used to annotate temporal expressions and provide them with a normalized value (e.g. 〈TIMEX tid=”t1” val=”2005-04-21”〉 April 21st, 2005 〈TIMEX〉); EVENT tags are used to annotate event expressions, providing “hooks” to relate them to other events and times introduced in the text (e.g. 〈EVENT eid=”ei”〉opened〈/EVENT〉); So-called TLINK tags indicate the temporal relations that hold between times and events (e.g. the stock market opened on April 21st, 2005 at 10:00pm 〈TLINK event=”e1” relatedTime=”t1” relation=INCLUDED-BY〉). Other tags are used to capture more subtle se-