Hostname: page-component-77c89778f8-n9wrp Total loading time: 0 Render date: 2024-07-18T18:20:29.486Z Has data issue: false hasContentIssue false

The LiLFeS Abstract Machine and its evaluation with the LinGO grammar

Published online by Cambridge University Press:  02 November 2000

YUSUKE MIYAO
Affiliation:
Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 Japan; e-mail: {usuke,mak}@is.s.u-tokyo.ac.jp
TAKAKI MAKINO
Affiliation:
Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 Japan; e-mail: {usuke,mak}@is.s.u-tokyo.ac.jp
KENTARO TORISAWA
Affiliation:
Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 Japan; Information and Human Behavior, PRESTO, Japan Science and Technology Corporation, 4-1-8 Kawaguchi Hon-cho, Kawaguchi-shi, Saitama 332-0012 Japan; e-mail: torisawa@is.s.u-tokyo.ac.jp
JUN-ICHI TSUJII
Affiliation:
Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 Japan; CCL, UMIST, P.O.Box 88, Manchester, M60 1QD, England; e-mail: tsujii@is.s.u-tokyo.ac.jp

Abstract

This article evaluates the efficiency of the LiLFeS abstract machine by performing parsing tasks with the LinGO English resource grammar. The instruction set of the abstract machine is optimized for efficient processing of definite clause programs and typed feature structures. LiLFeS also supports various tools required for efficient parsing (e.g. efficient copying, a built-in CFG parser) and the constructions of standard Prolog (e.g. cut, assertions, negation as failure). Several parsers and large-scale grammars, including the LinGO grammar, have been implemented in or ported to LiLFeS. Precise empirical results with the LinGO grammar are provided to allow comparison with other systems. The experimental results demonstrate the efficiency of the LiLFeS abstract machine.

Type
Research Article
Copyright
2000 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)