Part of speech tagging from a logical point of view

Abstract

This paper presents logical reconstructions of four different methods for part of speech tagging: Finite State Intersection Grammar, HMM tagging, Brill tagging, and Constraint Grammar. Each reconstruction consists of a first-order logical theory and an inference relation that can be applied to the theory, in conjunction with a description of data, in order to solve the tagging problem. The reconstructed methods are compared along a number of dimensions including ontology, expressive power, mode of reasoning, uncertainty, underspecification, and robustness. It is argued that logical reconstruction of NLP methods in general can lead to a deeper understanding of the knowledge and reasoning involved, and of the ways in which different methods are related.