Edmund Halley Road, Oxford Science Park
Oxford OX4 4GB
United Kingdom
Tel: +44 (0)1865 747711
Fax: +44 (0)1865 714170


Ph.D., October 1999
Department of Computer Science, University of Toronto, Toronto, Canada
M.Sc., October 1993
Department of Computer Science, University of Toronto, Toronto, Canada
B.Math, 1991
Faculty of Mathematics, University of Waterloo, Waterloo, Canada
Graduated with an Honours Co-op Degree - with distinction


Research into near-synonymy and lexical semantics with Prof. Graeme Hirst
University of Toronto, Toronto, Canada, ongoing HealthDoc: Tailored patient education systems
University of Waterloo, Waterloo, Canada, 1995-1997 Parallel numerical computing, Research Assistant
University of Waterloo, Waterloo, Canada, September-December 1990


Instructor: CSC148--Introduction to Computer Science
University of Toronto, Toronto, Canada
Summer 1996, summer 1997 Instructor: CSCA06--Introduction to Computer Programming
University of Toronto at Scarborough, Toronto, Canada
Fall 1997 Teaching Assistant
University of Waterloo, Waterloo, Canada, 1987-1991
University of Toronto, Toronto, Canada, 1991-1998


Software Designer, January-April 1990, May-August 1989
Mitel Corporation, Kanata, Ontario, Canada
Systems Engineering Rep.,, September-December 1988
Infomart, Ottawa, Canada
Programmer, January-April 1988, May-August 1987
Revenue Canada, Customs and Excise, Canada




Edmonds, Philip (1999). Semantic Representations of Near-Synonyms for Automatic Lexical Choice.
PhD thesis, published as technical report CSRI-399, Department of Computer Science, University of Toronto. [Thesis Abstract]

Edmonds, Philip (1998). Translating near-synonyms: Possibilities and preferences in the interlingua. In Proceedings of the AMTA/SIG-IL Second Workshop on Interlinguas, Langhorne, PA, pages 23-30. (Published in technical report MCCS-98-316, Computing Research Laboratory, New Mexico State University.)

Edmonds, Philip (1997). Choosing the Word Most Typical in Context Using a Lexical Co-occurrence Network. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL/EACL 97), Madrid, pages 507-509.

Edmonds, Philip (1996). Evoking Meaning by Choosing the Right Words. In Proceedings of the First Student Conference in Computational Linguistics in Montreal, pages 80-87.

Edmonds, Philip (1995). Lexical knowledge for natural language generation. PhD qualification paper. [unpublished manuscript]

Hirst, Graeme, Susan McRoy, Peter Heeman, Philip Edmonds, and Diane Horton (1994). Repairing conversational misunderstandings and non-understandings. Speech Communication 15:213-229.

Edmonds, Philip (1994). Collaboration on reference to objects that are not mutually known. In Proceedings of the 15th International Conference on Computational Linguistics (COLING-94), Kyoto, pages 1118-1122.

Edmonds, Philip (1993). A Computational Model of Collaboration on Reference in Direction-Giving Dialogues. MSc thesis, published as technical report CSRI-289, Department of Computer Science, University of Toronto. [Abstract] [ Appendix B]

Edmonds, Philip, Eleanor Chu, and Alan George (1993). Dynamic processing on a shared-memory multiprocessor. Parallel Computing 19:9-22.


Natural Languages: Programming Languages: Environments:


The central objective of my research is to develop a sophisticated computational lexical-choice process that can choose from a group of near-synonyms the one that best achieves the desired effects in the current context. The process is to be broadly applicable in machine translation and natural language generation systems. To achieve this, I argue that an explicit representation of the differences between near-synonyms is required, but not necessarily in a knowledge-based formalism. Thus, I investigate two complementary approaches.

In the first, differences between near-synonyms are represented as differences in the statistical co-occurrence of the near-synonyms with other words in large text corpora. Then, given a novel context and a set of near-synonyms to choose from, one can determine which near-synonym is the most typical choice.

The second approach is a traditional knowledge-based approach. As a result of studying the form and content of usage notes in synonym-discrimination dictionaries (e.g., Webster's New Dictionary of Synonyms), I identify several different components of fine-grained word meaning including denotation, style, attitude, and collocations. I then propose a clustered model of lexical knowledge that has two levels of representation: a core concept and peripheral concepts. All of the near-synonyms in a cluster share the core denotation, which is represented as a configuration of concepts defined in an ontology, and is used as a necessary applicability condition for each of the words. All differences between the words are represented explicitly as differences in the denotation, suggestion, or emphasis of peripheral concepts or as differences in style or attitude. The best word is chosen by finding the word that most closely matches (according to structural similarity and fuzzy similarity) a set of preferences for expressing certain ideas or using certain styles. The system is implemented in approximately 2000 lines of Lisp code.

Last modified: Tue Oct 05 1999