Blasting Open a Choice Space: Learning Inflectional Morphology for NLP

This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A run...

Full description

Saved in:
Bibliographic Details
Published in:Computational intelligence 2003-05, Vol.19 (2), p.111-135
Main Authors: McShane, Marjorie, Nirenburg, Sergei
Format: Article
Language:English
Subjects:
knowledge elicitation
machine translation
morphology
natural language processing
system architectures
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A running example throughout the text is taken from the paradigmatic morphology elicitation module of a system called Boas, which elicits knowledge to support a machine translation system. The main point of the article is an argument about the necessity to analyze the design choice space for complex natural language processing (NLP) systems early, comprehensively, and overtly.
ISSN:0824-7935
1467-8640
DOI:10.1111/1467-8640.00216