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. The view of grammar developed here is one in which unification is used for semantic interpretation, while purely formal agreement involves only a check for non-distinctness-i.e, variable-matching without variable substitution.

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 training the model, and 4) the process of understanding using the model. Finally, we give experimental results, including results on an ARPA evaluation.

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 directly from a sequence of spoken or written words. We present an overall description of the hidden understanding methodology, and discuss some of the critical implementation issues. Finally, we report on experimental results, including results of the December 1993 AR.PA evaluation.

the syntactically impossible antecedents. This latter This paper describes an implemented mechanism for handling bound anaphora, disjoint reference, and pronominal reference. The algorithm maps over every node in a parse tree in a left-to-right, depth first manner. Forward and backwards coreference, and disjoint reference are assigned during this tree walk. A semantic interpretation procedure is used to deal with multiple antecedents.

We present results from the February '92 evaluation on the ATIS travel planning domain for HARC, the BBN spoken language system (SLS). In addition, we discuss in detail the individual perfor-2. mance of BYBLOS, the speech recognition (SPREC) component.In the official scoring, conducted by NIST, BBN's HARC system 3. produced a weighted SLS score of 43.7 on all 687 evaluable utterances in the test set. This was the lowest error achieved by any of the 7 systems evaluated.4. For the SPREC evaluation BBN's BYBLOS system achieved a word error rate of 6.2% on the same 687 utterances and 9.4% on the entire test set of 971 utterances. These results were significantly better than any other speech system evaluated.

We describe HARC, a system for speech understanding that integrates speech recognition techniques with natural language processing. The integrated system uses statistical pattern recognition to build a lattice of potential words in the input speech. This word lattice is passed to a unification parser to derive all possible associated syntactic structures for these words. The resulting parse structures are passed to a multi-level semantics component for interpretation. 6 19a. NAME OF RESPONSIBLE PERSON

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