We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse.Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.

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We propose a distinction between two kinds of metonymy: "referential" metonymy, in which the referent of an NP is shifted, and "predicative" metonymy, in which the referent of the NP is unchanged and the argument place of the predicate is shifted instead. Examples are, respectively, "The hamburger is waiting for his check" and "Which airlines fly from Boston to Denver". We also show that complications arise for both types of metonymy when multiple coercing predicates are considered. Finally, we present implemented algorithms handling these complexities that generate both types of metonymic reading, as well as criteria for choosing one type of metonymic reading over another.

We present a computational treatment of the semantics of plural Noun Phrases which extends an earlier approach presented by Scha [7] to be able to deal with multiple-level plurals ("the boys and the girls", "the juries and the committees", etc.) 1 We argue that the arbitrary depth to which such plural structures can be nested creates a correspondingly arbitrary ambiguity in the possibilities for the distribution of verbs over such NPs. We present a recursive translation rule scheme which accounts for this ambiguity, and in particular show how it allows for the option of "partial distributivity" that collective verbs have when applied to such plural Noun Phrases.

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We describe the rst sentence understanding system that is completely based on learned methods both for understanding individual sentences, and determinig their meaning in the context of preceding sentences. We describe the models used for each of three stages in the understanding: semantic parsing, semantic classication, and discourse modeling. When we ran this system on the last test (December, 1994) of the ARPA Air Travel Information System (ATIS) task, we achieved 14.5% error rate. The error rate for those sentences that are context-independent (class A) was 9.5%.

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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.