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In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlike most previous work on event annotation, TimeML captures three distinct phenomena in temporal markup: (1) it systematically anchors(More)
We describe and evaluate hidden understanding models, a statistical learning approach to natural language understanding. Given a string of words, hidden understanding models determine the most likely meaning for the string. We discuss 1) the problem of representing meaning in this framework, 2) the structure of the statistical model, 3) the process of(More)
The problem of quantitatively comparing tile performance of different broad-coverage grammars of En-glish has to date resisted solution. Prima facie, known English grammars appear to disagree strongly with each other as to the elements of even tile simplest sentences. For instance, the grammars of Steve Abneying), Don tfindle (AT&T), Bob Ingria (BBN), and(More)
Current complex-feature based grammars use a single procedure-unification-for a multitude of purposes , among them, enforcing formal agreement between purely syntactic features. This paper presents evidence from several natural languages that unification-variable-matching combined with variable substitution-is the wrong mechanism for effecting agreement.(More)
This paper reports a handful of experiments designed to test the feasibility of applying well-known partial parsing techniques to the problem of automatic data base update from an open-ended source of messages, and the feasiblity of automatically learning semantic knowledge from annotated examples. The challenges arise from the incompleteness of any(More)
This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models derives from statistical models used in speech recognition, especially the use of hidden Markov models. These techniques are applied to the central problem of determining meaning(More)
APPROACH Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted linguisti c knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as par t of an ARPA-funded research effort on integrating probabilistic language models with more traditional linguisti c techniques. Our(More)