An introduction to language processing with perl and prolog

Cognitive Technologies Managing Editors: D M Gabbay J Siekmann Editorial Board: A Bundy J G Carbonell M Pinkal H Uszkoreit M Veloso W Wahlster M J Wooldridge Advisory Board: Luigia Carlucci Aiello Franz Baader Wolfgang Bibel Leonard Bolc Craig Boutilier Ron Brachman Bruce G Buchanan Anthony Cohn Artur d’Avila Garcez Luis Fariñas del Cerro Koichi Furukawa Georg Gottlob Patrick J Hayes James A Hendler Anthony Jameson Nick Jennings Aravind K Joshi Hans Kamp Martin Kay Hiroaki Kitano Robert Kowalski Sarit Kraus Maurizio Lenzerini Hector Levesque John Lloyd Alan Mackworth Mark Maybury Tom Mitchell Johanna D Moore Stephen H Muggleton Bernhard Nebel Sharon Oviatt Luis Pereira Lu Ruqian Stuart Russell Erik Sandewall Luc Steels Oliviero Stock Peter Stone Gerhard Strube Katia Sycara Milind Tambe Hidehiko Tanaka Sebastian Thrun Junichi Tsujii Kurt VanLehn Andrei Voronkov Toby Walsh Bonnie Webber Pierre M Nugues An Introduction to Language Processing with Perl and Prolog An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German With 153 Figures and 192 Tables 123 Author: Pierre M Nugues Institutionen för Datavetenskap Lunds Tekniska Högskola E-huset Ole Römers väg 223 63 Lund, Sweden Pierre.Nugues@cs.lth.se Managing Editors: Prof Dov M Gabbay Augustus De Morgan Professor of Logic Department of Computer Science, King’s College London Strand, London WC2R 2LS, UK Prof Dr Jörg Siekmann Forschungsbereich Deduktions- und Multiagentensysteme, DFKI Stuhlsatzenweg 3, Geb 43, 66123 Saarbrücken, Germany Library of Congress Control Number: 2005938508 ACM Computing Classification (1998): D.1.6, F.3, H.3, H.5.2, I.2.4, I.2.7, I.7, J.5 ISSN 1611-2482 ISBN-10 3-540-25031-X Springer Berlin Heidelberg New York ISBN-13 978-3-540-25031-9 Springer Berlin Heidelberg New York This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable for prosecution under the German Copyright Law Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Cover Design: KünkelLopka, Heidelberg Typesetting: by the Author Production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig Printed on acid-free paper 45/3100/YL 543210 À mes parents, À Madeleine Preface In the past 15 years, natural language processing and computational linguistics have considerably matured The move has mainly been driven by the massive increase of textual and spoken data and the need to process them automatically This dramatic growth of available data spurred the design of new concepts and methods, or their improvement, so that they could scale up from a few laboratory prototypes to proven applications used by millions of people Concurrently, speed and capacity of machines became an order of magnitude larger enabling us to process gigabytes of data and billions of words in a reasonable time, to train, test, retrain, and retest algorithms like never before Although systems entirely dedicated to language processing remain scarce, there are now scores of applications that, to some extent, embed language processing techniques The industry trend, as well as the user’s wishes, towards information systems able to process textual data has made language processing a new requirement for many computer science students This has shifted the focus of textbooks from readers being mostly researchers or graduate students to a larger public, from readings by specialists to pragmatism and applied programming Natural language processing techniques are not completely stable, however They consist of a mix that ranges from well mastered and routine to rapidly changing This makes the existence of a new book an opportunity as well as a challenge This book tries to take on this challenge and find the right balance It adopts a hands-on approach It is a basic observation that many students have difficulties to go from an algorithm exposed using pseudo-code to a runnable program I did my best to bridge the gap and provide the students with programs and ready-made solutions The book contains real code the reader can study, run, modify, and run again I chose to write examples in two languages to make the algorithms easy to understand and encode: Perl and Prolog One of the major driving forces behind the recent improvements in natural language processing is the increase of text resources and annotated data The huge amount of texts made available by Internet and the never-ending digitization led many of the practitioners to evolve from theory-oriented, armchair linguists to frantic empiricists This books attempts as well as it can to pay attention to this trend and VIII Preface stresses the importance of corpora, annotation, and annotated corpora It also tries to go beyond English-only and expose examples in two other languages, namely French and German The book was designed and written for a quarter or semester course At Lund, I used it when it was still under the form of lecture notes in the EDA171 course It comes with a companion web site where slides, programs, corrections, an additional chapter, and Internet pointers are available: www.cs.lth.se/˜pierre/ilppp/ All the computer programs should run with Perl available from www.perl.com or Prolog Although I only tested the programs with SWI Prolog available from www.swiprolog.org, any Prolog compatible with the ISO reference should apply Many people helped me during the last 10 years when this book took shape, stepby-step I am deeply indebted to my colleagues and to my students in classes at Caen, Nottingham, Stafford, Constance, and now in Lund Without them, it could never have existed I would like most specifically to thank the PhD students I supervised, in chronological order, Pierre-Olivier El Guedj, Christophe Godéreaux, Dominique Dutoit, and Richard Johansson Finally, my acknowledgments would not be complete without the names of the people I most cherish and who give meaning to my life, my wife, Charlotte, and my children, Andreas and Louise Lund, January 2006 Pierre Nugues Contents An Overview of Language Processing 1.1 Linguistics and Language Processing 1.2 Applications of Language Processing 1.3 The Different Domains of Language Processing 1.4 Phonetics 1.5 Lexicon and Morphology 1.6 Syntax 1.6.1 Syntax as Defined by Noam Chomsky 1.6.2 Syntax as Relations and Dependencies 1.7 Semantics 1.8 Discourse and Dialogue 1.9 Why Speech and Language Processing Are Difficult 1.9.1 Ambiguity 1.9.2 Models and Their Implementation 1.10 An Example of Language Technology in Action: the Persona Project 1.10.1 Overview of Persona 1.10.2 The Persona’s Modules 1.11 Further Reading 1 8 10 11 14 14 15 16 17 17 18 19 Corpus Processing Tools 2.1 Corpora 2.1.1 Types of Corpora 2.1.2 Corpora and Lexicon Building 2.1.3 Corpora as Knowledge Sources for the Linguist 2.2 Finite-State Automata 2.2.1 A Description 2.2.2 Mathematical Definition of Finite-State Automata 2.2.3 Finite-State Automata in Prolog 2.2.4 Deterministic and Nondeterministic Automata 2.2.5 Building a Deterministic Automata from a Nondeterministic One 23 23 23 24 26 27 27 28 29 30 31 X Contents 2.2.6 Searching a String with a Finite-State Automaton 2.2.7 Operations on Finite-State Automata Regular Expressions 2.3.1 Repetition Metacharacters 2.3.2 The Longest Match 2.3.3 Character Classes 2.3.4 Nonprintable Symbols or Positions 2.3.5 Union and Boolean Operators 2.3.6 Operator Combination and Precedence Programming with Regular Expressions 2.4.1 Perl 2.4.2 Matching 2.4.3 Substitutions 2.4.4 Translating Characters 2.4.5 String Operators 2.4.6 Back References Finding Concordances 2.5.1 Concordances in Prolog 2.5.2 Concordances in Perl Approximate String Matching 2.6.1 Edit Operations 2.6.2 Minimum Edit Distance 2.6.3 Searching Edits in Prolog Further Reading 31 33 35 36 37 38 39 41 41 42 42 42 43 44 44 45 46 46 48 50 50 51 54 55 Encoding, Entropy, and Annotation Schemes 3.1 Encoding Texts 3.2 Character Sets 3.2.1 Representing Characters 3.2.2 Unicode 3.2.3 The Unicode Encoding Schemes 3.3 Locales and Word Order 3.3.1 Presenting Time, Numerical Information, and Ordered Words 3.3.2 The Unicode Collation Algorithm 3.4 Markup Languages 3.4.1 A Brief Background 3.4.2 An Outline of XML 3.4.3 Writing a DTD 3.4.4 Writing an XML Document 3.4.5 Namespaces 3.5 Codes and Information Theory 3.5.1 Entropy 3.5.2 Huffman Encoding 3.5.3 Cross Entropy 59 59 60 60 61 63 66 2.3 2.4 2.5 2.6 2.7 66 67 69 69 69 71 74 75 76 76 77 80 Contents XI 3.5.4 Perplexity and Cross Perplexity Entropy and Decision Trees 3.6.1 Decision Trees 3.6.2 Inducing Decision Trees Automatically Further Reading 81 82 82 82 84 Counting Words 4.1 Counting Words and Word Sequences 4.2 Words and Tokens 4.2.1 What Is a Word? 4.2.2 Breaking a Text into Words: Tokenization 4.3 Tokenizing Texts 4.3.1 Tokenizing Texts in Prolog 4.3.2 Tokenizing Texts in Perl 4.4 N -grams 4.4.1 Some Definitions 4.4.2 Counting Unigrams in Prolog 4.4.3 Counting Unigrams with Perl 4.4.4 Counting Bigrams with Perl 4.5 Probabilistic Models of a Word Sequence 4.5.1 The Maximum Likelihood Estimation 4.5.2 Using ML Estimates with Nineteen Eighty-Four 4.6 Smoothing N -gram Probabilities 4.6.1 Sparse Data 4.6.2 Laplace’s Rule 4.6.3 Good–Turing Estimation 4.7 Using N -grams of Variable Length 4.7.1 Linear Interpolation 4.7.2 Back-off 4.8 Quality of a Language Model 4.8.1 Intuitive Presentation 4.8.2 Entropy Rate 4.8.3 Cross Entropy 4.8.4 Perplexity 4.9 Collocations 4.9.1 Word Preference Measurements 4.9.2 Extracting Collocations with Perl 4.10 Application: Retrieval and Ranking of Documents on the Web 4.11 Further Reading 87 87 87 87 88 89 89 91 92 92 93 93 95 95 95 97 99 99 100 101 102 103 104 104 104 105 105 106 106 107 108 109 111 Words, Parts of Speech, and Morphology 5.1 Words 5.1.1 Parts of Speech 5.1.2 Features 5.1.3 Two Significant Parts of Speech: The Noun and the Verb 113 113 113 114 115 3.6 3.7 XII Contents 5.2 5.3 5.4 5.5 5.6 5.7 Lexicons 117 5.2.1 Encoding a Dictionary 119 5.2.2 Building a Trie in Prolog 121 5.2.3 Finding a Word in a Trie 123 Morphology 123 5.3.1 Morphemes 123 5.3.2 Morphs 124 5.3.3 Inflection and Derivation 125 5.3.4 Language Differences 129 Morphological Parsing 130 5.4.1 Two-Level Model of Morphology 130 5.4.2 Interpreting the Morphs 131 5.4.3 Finite-State Transducers 131 5.4.4 Conjugating a French Verb 133 5.4.5 Prolog Implementation 134 5.4.6 Ambiguity 136 5.4.7 Operations on Finite-State Transducers 137 Morphological Rules 138 5.5.1 Two-Level Rules 138 5.5.2 Rules and Finite-State Transducers 139 5.5.3 Rule Composition: An Example with French Irregular Verbs 141 Application Examples 142 Further Reading 142 Part-of-Speech Tagging Using Rules 6.1 Resolving Part-of-Speech Ambiguity 6.1.1 A Manual Method 6.1.2 Which Method to Use to Automatically Assign Parts of Speech 6.2 Tagging with Rules 6.2.1 Brill’s Tagger 6.2.2 Implementation in Prolog 6.2.3 Deriving Rules Automatically 6.2.4 Confusion Matrices 6.3 Unknown Words 6.4 Standardized Part-of-Speech Tagsets 6.4.1 Multilingual Part-of-Speech Tags 6.4.2 Parts of Speech for English 6.4.3 An Annotation Scheme for Swedish 6.5 Further Reading 147 147 147 147 149 149 151 153 154 154 156 156 158 160 162 500 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M Nugues An Introduction to Language Processing with Perl and Prolog An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German With 153... Language Processing (Manning and Schütze 1999), Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (Jurafsky and Martin... computer to understand texts or speech and to interact accordingly with human beings Understanding or translating texts automatically and talking to an artificial conversational assistant are major