Parsing with Typed Feature Structures

TL;DR

This paper introduces a parsing approach based on typed feature structures (TFS), using an abstract machine and novel constructs like multi-rooted feature structures (MRSs) to improve grammatical representation and parsing correctness.

Contribution

It develops an operational semantics for TFS-based grammars, introduces AFSs and MRSs for enhanced representation, and presents a correct bottom-up chart parser as a computational model.

Findings

Defined unification over abstract typed feature structures (AFSs)

Introduced multi-rooted feature structures (MRSs) for grammar representation

Presented a correct bottom-up chart parser for TFS-based grammars

Abstract

In this paper we provide for parsing with respect to grammars expressed in a general TFS-based formalism, a restriction of ALE. Our motivation being the design of an abstract (WAM-like) machine for the formalism, we consider parsing as a computational process and use it as an operational semantics to guide the design of the control structures for the abstract machine. We emphasize the notion of abstract typed feature structures (AFSs) that encode the essential information of TFSs and define unification over AFSs rather than over TFSs. We then introduce an explicit construct of multi-rooted feature structures (MRSs) that naturally extend TFSs and use them to represent phrasal signs as well as grammar rules. We also employ abstractions of MRSs and give the mathematical foundations needed for manipulating them. We then present a simple bottom-up chart parser as a model for computation: grammars written in the TFS-based formalism are executed by the parser. Finally, we show that the parser is correct.