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Natural language is easy for people and hard for machines. For two
generations, the tantalizing goal has been to get computers to
handle human languages in ways that will be compelling and useful
to people. Obstacles are many and legendary. Natural Language
Processing: The PLNLP Approach describes one group's decade of
research in pursuit of that goal. A very broad coverage NLP system,
including a programming language (PLNLP) development tools, and
analysis and synthesis components, was developed and incorporated
into a variety of well-known practical applications, ranging from
text critiquing (CRITIQUE) to machine translation (e.g. SHALT).
This books represents the first published collection of papers
describing the system and how it has been used. Twenty-six authors
from nine countries contributed to this volume. Natural language
analysis, in the PLNLP approach, is done is six stages that move
smoothly from syntax through semantics into discourse. The initial
syntactic sketch is provided by an Augmented Phrase Structure
Grammar (APSG) that uses exclusively binary rules and aims to
produce some reasonable analysis for any input string. Its
`approximate' analysis passes to the reassignment component, which
takes the default syntactic attachments and adjusts them, using
semantic information obtained by parsing definitions and example
sentences from machine-readable dictionaries. This technique is an
example of one facet of the PLNLP approach: the use of natural
language itself as a knowledge representation language -- an
innovation that permits a wide variety of online text materials to
be exploited as sources of semantic information. The next stage
computes the intrasential argument structure and resolves all
references, both NP- and VP-anaphora, that can be treated at this
point in the processing. Subsequently, additional components,
currently not so well developed as the earlier ones, handle the
further disambiguation of word senses, the normalization of
paraphrases, and the construction of a paragraph (discourse) model
by joining sentential semantic graphs. Natural Language Processing:
The PLNLP Approach acquaints the reader with the theory and
application of a working, real-world, domain-free NLP system, and
attempts to bridge the gap between computational and theoretical
models of linguistic structure. It provides a valuable resource for
students, teachers, and researchers in the areas of computational
linguistics, natural processing, artificial intelligence, and
information science.
This book constitutes the refereed proceedings of the 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002, held in Tiburon, CA, USA, in October 2002.The 18 revised full technical papers, 3 user studies, and 9 system descriptions presented were carefully reviewed and selected for inclusion in the book. Among the issues addressed are hybrid translation environments, resource-limited MT, statistical word-level alignment, word formation rules, rule learning, web-based MT, translation divergences, example-based MT, data-driven MT, classification, contextual translation, the lexicon building process, commercial MT systems, speeck-to-speech translation, and language checking systems.
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