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Machine translation of natural languages is one of the most
complex and comprehensive applications of computational linguistics
and artificial intelligence. This is especially true of
knowledge-based machine translation (KBMT) systems, which require
many knowledge resources and processing modules to carry out the
necessary levels of analysis, representation and generation of
meaning and form. The number of real-world problems, tasks, and
solutions involved in developing any realistic-size knowledge-based
machine translation system is enormous. It is thus difficult for
researchers in the field to learn what a system "really
does." This book fills that need with a detailed case study of a KBMT
system implemented at the Center for Machine Translation at
Carnegie Mellon University. The research consists in part of the
creation of a system for translation between English and Japanese.
The corpora used in the project were manuals for installing and
maintaining IBM personal computers (sponsorship by IBM, through its
Tokyo Research Laboratory) Individual chapters describe the
interlingua texts used in knowledge-based machine translation, the
grammar formalism embodied in the system, the grammars and lexicons
and their roles in the translation process, the process of source
language analysis, an augmentation module that interactively and
automatically resolves ambiguities remaining after source language
analysis, and the generator, which produces target language
sentences. Detailed appendices illustrate the process from analysis
through generation. This book is intended for developers, researchers and advanced students in natural language processing and computational linguistics, including all those who have an interest in machine translation and machine-aided translation.
A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
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