tailieunhanh - Báo cáo khoa học: "Event Matching Using the Transitive Closure of Dependency Relations"

This paper describes a novel event-matching strategy using features obtained from the transitive closure of dependency relations. The method yields a model capable of matching events with an F-measure of . training and test instance in a feature space. Conceptually, our features are of three different varieties. This section describes the first two kinds, which we call “low-level” features, in that they attempt to capture how much of the basic information of an event e is present in a sentence s. Lexical features . | Event Matching Using the Transitive Closure of Dependency Relations Daniel M. Bikel and Vittorio Castelli IBM T. J. Watson Research Center 1101 Kitchawan Road Yorktown Heights NY 10598 dbikel vittorio @ Abstract This paper describes a novel event-matching strategy using features obtained from the transitive closure of dependency relations. The method yields a model capable of matching events with an F-measure of . 1 Introduction Question answering systems are evolving from their roots as factoid or definitional answering systems to systems capable of answering much more open-ended questions. For example it is one thing to ask for the birthplace of a person but it is quite another to ask for all locations visited by a person over a specific period of time. Queries may contain several types of arguments person organization country location etc. By far however the most challenging of the argument types are the event or topic arguments where the argument text can be a noun phrase a participial verb phrase or an entire indicative clause. For example the following are all possible event arguments the . invasion of Iraq Red Cross admitting Israeli and Palestinian groups GM offers buyouts to union employees In this paper we describe a method to match an event query argument to the sentences that mention that event. That is we seek to model p s contains e s e where e is a textual description of an event such as an event argument for a GALE distillation query and where s is an arbitrary sentence. In the first example above the . invasion of Iraq such a model should produce a very high score for that event description and the sentence The . invaded Iraq in 2003. 2 Low-level features As the foregoing implies we are interested in training a binary classifier and so we represent each training and test instance in a feature space. Conceptually our features are of three different varieties. This section describes the first two kinds which we call low-level