ROGET’S THESAURUS - AS A LEXICAL RESOURCE FOR NATURAL LANGUAGE PROCESSING - Thesis (Master of Computer Science)

Aug 20, 2026

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WordNet demonstrated that large-scale electronic lexical knowledge bases can be built from lexical semantics and supports tasks such as information retrieval, language generation, question answering, text categorization, text classification, and word sense disambiguation. Motivated by WordNet’s success, this thesis proposes an alternative lexical resource based on the 1987 Penguin edition of Roget’s Thesaurus of English Words and Phrases. It presents a machine-tractable implementation, describes transformation steps from machine-readable files into efficient structures and classes, studies Roget’s organization, and contrasts it with WordNet.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages231

Reading Time9 h 42 min

AccessFree to download

TABLE OF CONTENTS

1 INTRODUCTION

2 THE USE OF THESAURI IN NATURAL LANGUAGE PROCESSING

ROGET’S THESAURUS AS A LEXICAL RESOURCE FOR NATURAL LANGUAGE PROCESSING Mario Jarmasz Thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of Master of Computer Science July, 2003 Ottawa-Carleton Institute for Computer Science School of Information Technology and Engineering University of Ottawa Ottawa, Ontario, Canada © Mario Jarmasz, Ottawa, Canada, 2003 ABSTRACT WordNet proved that it is possible to construct a large-scale electronic lexical database on the principles of lexical semantics. It has been accepted and used extensively by computational linguists ever since it was released. Some of its applications include information retrieval, language generation, question answering, text categorization, text classification and word sense disambiguation. Inspired by WordNet's success, we propose as an alternative a similar resource, based on the 1987 Penguin edition ofRoget’s Thesaurus of English Words and Phrases. Peter Mark Roget published his first Thesaurus over 150 years ago. Countless writers, oratorsand students of the English language have used it. Computational linguists have employed Roget’s for almost 50 years in Natural Language Processing. Some of the tasks they have used it for include machine translation, computing lexical cohesion in texts and constructing databases that can infer common sense knowledge. This dissertation presents Roget’s merits by explaining what it really is and how it has been used, while comparing its applications to those of WordNet. The NLP community has hesitated in accepting Roget’s Thesaurus because a proper machinetractable version was not available. This dissertation presents an implementation of a machine-tractable version of the 1987 Penguin edition of Roget’s Thesaurus – the first implementation of its kind to use an entire current edition. It explains the steps necessary for taking a machine-readable file and transforming it into a tractable system. This involves converting the lexical material into a format that can be more easily exploited, identifying data structures and designing classes to computerize the Thesaurus. Roget’s organization is studied in detail and contrasted with WordNet’s. We show two applications of the computerized Thesaurus: computing semantic similarity between words and phrases, and building lexical chains in a text. The experiments are performed using well-known benchmarks and the results are compared to those of other systems that use Roget’s, WordNet and statistical techniques. Roget’s has turned out to be an excellent resource for measuring semantic similarity; lexical chains are easily built but more difficult to evaluate. We also explain ways in which Roget’s Thesaurus and WordNet can be combined. To my parents, who are my most valued treasure. TABLE OF CONTENTS 1 INTRODUCTION..............................................................................................................................................1 1.1 LEXICAL RESOURCES FOR NATURAL LANGUAGE PROCESSING ................................................................... 1 1.2 ELECTRONIC LEXICAL KNOWLEDGE BASES ................................................................................................2 1.3 AN INTRODUCTION TO ROGET’S THESAURUS ...............................................................................................2 1.3.1 The Roget’s Electronic Lexical Knowledge Base .................................................................................. 3 1.4 GOALS OF THIS THESIS ................................................................................................................................3 1.5 ORGANIZATION OF THE THESIS ...................................................................................................................4 1.5.1 Paper Map ........................................................................

FAQs

What lexical-semantic resources does the thesis compare and why?

It compares Roget’s Thesaurus and WordNet. The work contrasts their NLP applications and explains how a machine-tractable Roget’s can address limitations in wider adoption.

What is the main contribution of the thesis regarding Roget’s Thesaurus?

The thesis presents an implementation of a machine-tractable version of the 1987 Penguin edition. It details how to transform machine-readable lexical material into a tractable system with appropriate data structures and classes.

What applications and evaluations are demonstrated for the computerized Thesaurus?

Two applications are presented: computing semantic similarity between words and phrases, and building lexical chains in a text. Experiments use established benchmarks and compare results with systems using Roget’s, WordNet, and statistical techniques.

Aug 20, 2026

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