Tooling the Lexicon Acquisition Process for Large-Scale KBMT

Large-scale lexical knowledge acquisition is one of the most time critical steps in developing a knowledge-based machine translation system. In particular, developing the syntactic lexicon for the target language can be an unwieldy task, as on-line knowledge assets are likely to be more scarce than for the source language. This paper addresses this problem within the KANT machine translation system and describes how we structure the KA process to address this problem. This was done by first determining the nature of the desired process and then developing tools to implement that process. The tools themselves and the ways in which the helped us to realize out design goals are described. We conclude that, while the problem of lexical acquisition can be formidable, it can be overcome with proper foresight and tool design.

• Type: Conference
• Year: 1994
• Where: ICTAI
• Authors: John R. R. Leavitt, Deryle W. Lonsdale, Kevin Keck, Eric Nyberg
• Keywords: Artificial Intelligence, ICTAI 1994, Knowledge-based Machine Translation, Lexical Knowledge Acquisition, Machine Translation System