Enriching a Meta-Language With Higher-Order Features

Various meta-languages for the manipulation and specification of programs and programming languages have recently been proposed. We examine one such framework, called natural semantics, which was inspired by the work of G. Plotkin on operational semantics and extended by G. Kahn and others at INRIA. Natural semantics makes use of a first-order meta-language which represents programs as first-order tree structures and reasons about these using natural deduction-like methods. We present the following three enrichments of this meta-language. First, programs are represented not by first-order structures but by simply typed λ-terms. Second, schema variables in inference rules can be higher-order variables. Third, the reasoning mechanism is explicitly extended with proof methods which have proved valuable for natural deduction systems. In particular, we add methods for introducing and discharging assumptions and for introducing and discharging parameters. The first method can be used to prove hypothetical propositions while the second can be used to prove generic or universal propositions. We provide several example specifications using this extended meta-language and compare them to their first-order specifications. We argue that our extension yields a more natural and powerful meta-language than the related first-order system. We outline how this enriched meta-language can be compiled into the higher-order logic programming language λProlog

A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature

Over the past 50 years many have debated what representation should be used to capture the meaning of natural language utterances. Recently new needs of such representations have been raised in research. Here I survey some of the interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure