This paper proposes a perceptually motivated method for solving proportional analogy problems involving sequential patterns. Our method is based on an algebraic model of pattern perception that determines the gestalt structure of sequential patterns. The gestalts of sequential patterns are represented as algebraic terms in the model. An analogical relation between sequential patterns appearing in proportional analogy is then formalized as a mapping between algebras that generate terms representing the gestalts of these patterns. Based on this formalism, an algorithm is proposed that solves proportional analogy problems: given three sequential patterns, the algorithm computes a fourth pattern such that the resulting two pairs of patterns are perceived as having an identical analogical relation.

In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. This approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Tree-bank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method. A data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the English ATIS corpus and the Dutch OVIS corpus. Experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.

This paper decribes a computational treatment of the semantics of relational nouns. It covers relational nouns such as "sister" and "commander", and focuses especially on a particular subcategory of them, called function nouns ("speed", "distance", "rating"). Relational nouns are usually viewed as either requiring non-compositional semantic interpretation, or causing an undesirable proliferation of syntactic rules. In contrast to this, we present a treatment which is both syntactically uniform and semantically compositional. The core ideas of this treatment are: (1) The recognition of different levels of semantic analysis; in particular, the distinction between an English-oriented and a domain-oriented level of meaning representation. (2) The analysis of relational nouns as denoting relation-extensions.The paper shows how this approach handles a variety of linguistic constructions involving relational nouns. The treatment presented here has been implemented in BBN's Spoken Language System, an experimental spoken language interface to a database/graphics system.

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.), 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.

A correct structural analysis of a discourse is a prerequisite for understanding it. This paper sketches the outline of a discourse grammar which acknowledges several different levels of structure. This grammar, the "Dynamic Discourse Model", uses an Augmented Transition Network parsing mechanism to build a representation of the semantics of a discourse in a stepwise fashion, from left to right, on the basis of the semantic representations of the individual clauses which constitute the discourse. The intermediate states of the parser model the intermediate states of the social situation which generates the discourse.The paper attempts to demonstrate that a discourse may indeed be viewed as constructed by means of sequencing and recursive nesting of discourse constituents. It gives rather detailed examples of discourse structures at various levels, and shows how these structures are described in the framework proposed here.