Categorial type logics developed out of the Syntactic Calculus proposed by Lambek fifty years ago, and complemented in the 1980'ies with a ‘proofs-as-programs’ interpretation associating derivations in a syntactic source calculus with terms of the simply typed linear lambda calculus expressing meaning composition.

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Categorial Type Logics

Functional Grammar describes grammar in functional terms in which a language is interpreted as a system of meanings. The language system consists of three macro-functions known as meta-functional components: the interpersonal function, the ideational function, and the textual function, all of which make a contribution to the structure of a text. The concepts discussed in Functional Grammar aims at giving contribution to the understanding of a text and evaluation of a text, which can be applied for text analysis. Using the concepts in Functional Grammar, English teachers may help the students learn how various grammatical features and grammatical systems are used in written texts so that they can read and write better.

A Functional Grammar

This article offers a survey of computational research on referring expression generation (REG). It introduces the REG problem and describes early work in this area, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years. We discuss computational frameworks underlying REG, and demonstrate a recent trend that seeks to link REG algorithms with well-established Knowledge Representation techniques. Considerable attention is given to recent efforts at evaluating REG algorithms and the lessons that they allow us to learn. The article concludes with a discussion of the way forward in REG, focusing on references in larger and more realistic settings.

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Computational generation of referring expressions: A survey

I present an architecture for data-to-text systems, that is NLG systems which produce texts from non-linguistic input data; this essentially extends the architecture of Reiter and Dale (2000) to systems whose input is raw data instead of AI knowledge bases This architecture is being used in the BabyTalk project, and is based on experiences in several projects at Aberdeen; it also seems to be compatible with many data-to-text systems developed elsewhere It consists of four stages which are organised in a pipeline: Signal Analysis, Data Interpretation, Document Planning, and Microplanning and Realisation

An Architecture for Data-to-Text Systems

In this paper we present recent work on GATE, a widely-used framework and graphical development environment for creating and deploying Language Engineering components and resources in a robust fashion. The GATE architecture has facilitated the development of a number of successful applications for various language processing tasks (such as Information Extraction, dialogue and summarisation), the building and annotation of corpora and the quantitative evaluations of LE applications. The focus of this paper is on recent developments in response to new challenges in Language Engineering: Semantic Web, integration with Information Retrieval and data mining, and the need for machine learning support.

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Evolving GATE to meet new challenges in language engineering

We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

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A Reference Architecture for Natural Language Generation Systems

We present an algorithm for the generation of sentences from the semantic representations of Unification Categorial Grammar. We discuss a variant of Shieber's semantic monotonicity requirement and its utility in our algorithm. We indicate how the algorithm may be extended to other grammars obeying the same requirement. Appendices contain a full listing of the program and a trace of execution of the algorithm.

An algorithm for generation in Unification Categorial Grammar

This paper shows how higher levels of generalization can be introduced into unification grammars by exploiting methods for typing grammatical objects. We discuss the strategy of using global declarations to limit possible linguistic structures, and sketch a few unusual aspects of our type-checking algorithm. We also describe the sort system we use in our semantic representation language and illustrate the expressive power gained by being able to state global constraints over these sorts. Finally, we briefly illustrate the sort system by applying it to some agreement phenomena and to problems of adjunct resolution.

Expressing generalizations in unification-based grammar formalisms

In this paper we present a logical treatment of semifree word order and bounded discontinuous constituency. We extend standard feature value logics to treat word order in a single formalism with a rigorous semantics without phrase structure rules. The elimination of phrase structure rules allows a natural generalisation of the approach to nonconfigurational word order and bounded discontinuous continuency via sequence union. Sequence union formalises the notions of clause union and scrambling by providing a mechanism for describing word order domains larger than the local tree. The formalism incorporates the distinction between bounded and unbounded forms of discontinuous constituency. Grammars are organised as algebraic theories. This means that linguistic generalisations are stated as axioms about the structure of signs. This permits a natural interpretation of implicational universals in terms of theories, subtheories and implicational axioms. The accompanying linguistic analysis is eclectic, borrowing insights from many current linguistic theories.

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A logical treatment of semi-free word order and bounded discontinuous constituency

We present the RAGS (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

Mike Reape

Papers

A Reference Architecture for Natural Language Generation Systems

An algorithm for generation in Unification Categorial Grammar

Expressing generalizations in unification-based grammar formalisms

A logical treatment of semi-free word order and bounded discontinuous constituency

A Reference Architecture for Generation Systems