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This paper presents an algorithm for identifying the noun phrase antecedents of third person pronouns and lexical anaphors (reflexives and reciprocals). The algorithm applies to the syntactic representations generated by McCord's Slot Grammar parser, and relies on salience measures derived from syntactic structure and a simple dynamic model of attentional(More)
In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focussed on one of the(More)
We propose a probabilistic type theory in which a situation s is judged to be of a type T with probability p. In addition to basic and functional types it includes , inter alia, record types and a notion of typing based on them. The type system is intensional in that types of situations are not reduced to sets of situations. We specify the fragment of a(More)
This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We extract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine(More)