论文信息 - Building a Large Ontology for Machine Translation

Building a Large Ontology for Machine Translation

This paper describes efforts underway to construct a large-scale ontology to support semantic processing in the PAN-GLOSS knowledge-base machine translation system. Because we are aiming at broad semantic coverage, we are focusing on automatic and semi-automatic methods of knowledge acquisition. Here we report on algorithms for merging complementary online resources, in particular the LDOCE and WordNet dictionaries. We discuss empirical results, and how these results have been incorporated into the PANGLOSS ontology.

Kevin Knight | Kevin Knight

[1] Sergei Nirenburg,et al. World Modeling for NLP , 1990 .

[2] George A. Miller,et al. Introduction to WordNet: An On-line Lexical Database , 1990 .

[3] Ramanathan V. Guha,et al. Building large knowledge-based systems , 1989 .

[4] Yorick Wilks,et al. ULTRA: A Multi-lingual Machine Translator , 1991, MTSUMMIT.

[5] John A. Bateman. Upper Modeling: organizing knowledge for natural language processing , 1990, INLG.

[6] Louise Guthrie,et al. Disambiguation: a Study in Weighted Preference* , 2022 .

[7] Philip Resnik,et al. WordNet and Distributional Analysis: A Class-based Approach to Lexical Discovery , 1992, AAAI 1992.

[8] Robert L. Mercer,et al. Class-Based n-gram Models of Natural Language , 1992, CL.

[9] Paul Procter,et al. Longman Dictionary of Contemporary English , 1978 .

[10] Ramanathan V. Guha,et al. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[11] Elizabeth D. Liddy,et al. Use of Subject Field Codes from a Machine-Readable Dictionary for Automatic Classification of Documents , 1992 .