Description
Book SynopsisThis book is at the very heart of linguistics. It provides the theoretical and methodological framework needed to create a successful linguistic project.
Potential applications of descriptive linguistics include spell-checkers, intelligent search engines, information extractors and annotators, automatic summary producers, automatic translators, and more. These applications have considerable economic potential, and it is therefore important for linguists to make use of these technologies and to be able to contribute to them.
The author provides linguists with tools to help them formalize natural languages and aid in the building of software able to automatically process texts written in natural language (Natural Language Processing, or NLP).
Computers are a vital tool for this, as characterizing a phenomenon using mathematical rules leads to its formalization. NooJ – a linguistic development environment software developed by the author – is described and practically applied to examples of NLP.
Trade ReviewThis book lays ground for better understanding of both computational linguistics (CL) and natural language processing (NLP) perspectives, i.e. it shows how to describe language (CL) in order to build the best NLP applications (NLP). The book bridges the gap between theoretical linguistic phenomena and practical language models. It shows how computational linguists and language engineers working together can bring us closer to better language understanding by both humans and computers.
The author takes us on a stroll through the layers of language processing, explaining very soundly and giving examples and counterexamples that bring additional clarification for each step we make on that path. Starting with the tiny bits of written language, the alphabet, via dictionary and atomic linguistic units that occupy it, he clarifies the importance of each step, giving us solid ground to build upon any language project we might venture to undertake.
Silberztein knows how to invite an audience into his Project, as he calls it, and introduces the topic in such a manner that makes you want to read the book until the last page (and solve all the CL and NLP problems on the way). He smoothly transitions through Parts one, two and three, building one topic upon the previous one, as if playing with lego blocks.
He begins by demonstrating the importance of defining basic (atomic) linguistic units starting with the alphabet and vocabulary that prepare us for the construction of electronic dictionaries. It is the design of the e-dictionary that will allow us and support us in formalizing the language of our interest. Thus, it is not a surprise that a thorough classification and understanding of our basic resources is needed to prepare (and prepare well) and specify affixes [re-, de-, un-, -ation], simple words [home, love, sky], multiword units [sweet potatoes, more and more, round table] and expressions [to give up, to turn off, to take off] that we will play around with to construct and annotate new words, phrases and sentences.
He then takes regular grammars, context-free grammars, context-sensitive grammars and unrestricted grammars and he makes them all work via NooJ’s multifaceted approach. The (beautiful) simplicity of this application is aligned with the way we, as humans, process vocabulary, grammar, orthography, syntax, semantics…thus making the NooJ as a tool easy to use by beginners and more advanced users alike.
It is only expected that the journey will end with applications both in parsing and generating written text. We are presented with the lexical analysis, syntactic analysis (local and structural) and transformational analysis that open up the door for more sophisticated NLP applications (Question Answering, Machine Translation, Semantic Analyzer, etc.)
The most expected audience of ‘"Formalizing Natural Languages: The NooJ Approach’ are linguists i.e. computational linguists and NLP people (or as the author likes to call them language engieers). But, since the book holds the key that can open a whole sea of possible applications in the domains of other subfields, I would recommend it to etymologists, sociolinguists, psycholinguists, forensic linguists, internet linguists, corpus linguists or to any data scientist today. Having each chapter end with exercises and additional internet links, the book is also suitable as a class reading in NLP and CL classes, machine translation and similar. The book is presented in a way as to improve the understanding of the ways the natural language can be formalized and has the power to reveal some new applications to almost any type of written text. Since the book and NooJ as a tool came into existence in the era dominated by unstructured data, the potential of presented tool is limited only by the imagination of its user.
—Kristina Kocijan, Department of Information and Communication Sciences Faculty of Humanities and Social Sciences University of Zagreb, Croatia
Table of ContentsAcknowledgments xi
Chapter 1. Introduction: the Project 1
1.1. Characterizing a set of infinite size 4
1.2. Computers and linguistics 5
1.3. Levels of formalization 6
1.4. Not applicable 7
1.4.1. Poetry and plays on words 7
1.4.2. Stylistics and rhetoric 9
1.4.3. Anaphora, coreference resolution, and semantic disambiguation 10
1.4.4. Extralinguistic calculations 12
1.5. NLP applications 12
1.5.1. Automatic translation 14
1.5.2. Part-of-speech (POS) tagging 18
1.5.3. Linguistic rather than stochastic analysis 27
1.6. Linguistic formalisms: NooJ 27
1.7. Conclusion and structure of this book 30
1.8. Exercises 31
1.9. Internet links 32
Part 1. Linguistic Units 35
Chapter 2. Formalizing the Alphabet 37
2.1. Bits and bytes 37
2.2. Digitizing information 39
2.3. Representing natural numbers 39
2.3.1. Decimal notation 39
2.3.2. Binary notation 40
2.3.3. Hexadecimal notation 41
2.4. Encoding characters 41
2.4.1. Standardization of encodings 43
2.4.2. Accented Latin letters, diacritical marks, and ligatures 45
2.4.3. Extended ASCII encodings 46
2.4.4. Unicode 47
2.5. Alphabetical order 53
2.6. Classification of characters 56
2.7. Conclusion 56
2.8. Exercises 57
2.9. Internet links 57
Chapter 3. Defining Vocabulary 59
3.1. Multiple vocabularies and the evolution of vocabulary 59
3.2. Derivation 63
3.2.1. Derivation applies to vocabulary elements 63
3.2.2. Derivations are unpredictable 64
3.2.3. Atomicity of derived words 65
3.3. Atomic linguistic units (ALUs) 67
3.3.1. Classification of ALUs 67
3.4. Multiword units versus analyzable sequences of simple words 70
3.4.1. Semantics 72
3.4.2. Usage 76
3.4.3. Transformational analysis 77
3.5. Conclusion 80
3.6. Exercises 81
3.7. Internet links 81
Chapter 4. Electronic Dictionaries 83
4.1. Could editorial dictionaries be reused? 83
4.2. LADL electronic dictionaries 90
4.2.1. Lexicon-grammar 90
4.2.2. DELA 93
4.3. Dubois and Dubois-Charlier electronic dictionaries 94
4.3.1. The Dictionnaire électronique des mots 95
4.3.2. Les Verbes Français (LVF) 97
4.4. Specifications for the construction of an electronic dictionary 99
4.4.1. One ALU = one lexical entry 99
4.4.2. Importance of derivation 100
4.4.3. Orthographic variation 101
4.4.4. Inflection of simple words, compound words, and expressions 103
4.4.5. Expressions 104
4.4.6. Integration of syntax and semantics 104
4.5. Conclusion 107
4.6. Exercises 108
4.7. Internet links 108
Part 2. Languages, Grammars and Machines 111
Chapter 5. Languages, Grammars, and Machines 113
5.1. Definitions 113
5.1.1. Letters and alphabets 113
5.1.2. Words and languages 114
5.1.3. ALU, vocabularies, phrases, and languages 114
5.1.4. Empty string 115
5.1.5. Free language 116
5.1.6. Grammars 116
5.1.7. Machines 117
5.2. Generative grammars 118
5.3. Chomsky-Schützenberger hierarchy 119
5.3.1. Linguistic formalisms 122
5.4. The NooJ approach 124
5.4.1. A multifaceted approach 124
5.4.2. Unified notation 125
5.4.3. Cascading architecture 127
5.5. Conclusion 127
5.6. Exercises 128
5.7. Internet links 129
Chapter 6. Regular Grammars 131
6.1. Regular expressions 131
6.1.1. Some examples of regular expressions 135
6.2. Finite-state graphs 137
6.3. Non-deterministic and deterministic graphs 139
6.4. Minimal deterministic graphs 141
6.5. Kleene’s theorem 142
6.6. Regular expressions with outputs and finite-state transducers 146
6.7. Extensions of regular grammars 151
6.7.1. Lexical symbols 151
6.7.2. Syntactic symbols 153
6.7.3. Symbols defined by grammars 154
6.7.4. Special operators 155
6.8. Conclusion 159
6.9. Exercises 159
6.10. Internet links 159
Chapter 7. Context-Free Grammars 161
7.1. Recursion 164
7.1.1. Right recursion 166
7.1.2. Left recursion 167
7.1.3. Middle recursion 168
7.2. Parse trees 170
7.3. Conclusion 173
7.4. Exercises 173
7.5. Internet links 174
Chapter 8. Context-Sensitive Grammars 175
8.1. The NooJ approach 176
8.1.1. The anbncn language 177
8.1.2. The language a2n 180
8.1.3. Handling reduplications 181
8.1.4. Grammatical agreements 182
8.1.5. Lexical constraints in morphological grammars 185
8.2. NooJ contextual constraints 186
8.3. NooJ variables 188
8.3.1. Variables’ scope 188
8.3.2. Computing a variable’s value 189
8.3.3. Inheriting a variable’s value 191
8.4. Conclusion 191
8.5. Exercises 192
8.6. Internet links 192
Chapter 9. Unrestricted Grammars 195
9.1. Linguistic adequacy 197
9.2. Conclusion 199
9.3. Exercise 199
9.4. Internet links 199
Part 3. Automatic Linguistic Parsing 201
Chapter 10. Text Annotation Structure 205
10.1. Parsing a text 205
10.2. Annotations 206
10.2.1. Limits of XML/TEI representation 207
10.3. Text annotation structure (TAS) 208
10.4. Exercise 211
10.5. Internet links 212
Chapter 11. Lexical Analysis 213
11.1. Tokenization 213
11.1.1. Letter recognition 214
11.1.2. Apostrophe/quote 217
11.1.3. Dash/hyphen 219
11.1.4. Dot/period/point ambiguity 222
11.2. Word forms 224
11.2.1. Space and punctuation 224
11.2.2. Numbers 226
11.2.3. Words in upper case 228
11.3. Morphological analyses 229
11.3.1. Inflectional morphology 230
11.3.2. Derivational morphology 234
11.3.3. Lexical morphology 236
11.3.4. Agglutinations 239
11.4. Multiword unit recognition 241
11.5. Recognizing expressions 243
11.5.1. Characteristic constituent 244
11.5.2. Varying the characteristic constituent 245
11.5.3. Varying the light verb 246
11.5.4. Resolving ambiguity 247
11.5.5. Annotating expressions 251
11.6. Conclusion 254
11.7. Exercise 255
Chapter 12. Syntactic Analysis 257
12.1. Local grammars 257
12.1.1. Named entities 257
12.1.2. Grammatical word sequences 262
12.1.3. Automatically identifying ambiguity 263
12.2. Structural grammars 265
12.2.1. Complex atomic linguistic units 266
12.2.2. Structured annotations 268
12.2.3. Ambiguities 270
12.2.4. Syntax trees vs parse trees 273
12.2.5. Dependency grammar and tree 276
12.2.6. Resolving ambiguity transparently 279
12.3. Conclusion 280
12.4. Exercises 281
12.5. Internet links 281
Chapter 13. Transformational Analysis 283
13.1. Implementing transformations 286
13.2. Theoretical problems 292
13.2.1. Equivalence of transformation sequences 292
13.2.2. Ambiguities in transformed sentences 293
13.2.3. Theoretical sentences 294
13.2.4. The number of transformations to be implemented 295
13.3. Transformational analysis with NooJ 297
13.3.1. Applying a grammar in “generation” mode 298
13.3.2. The transformation’s arguments 299
13.4. Question answering 303
13.5. Semantic analysis 304
13.6. Machine translation 305
13.7. Conclusion 309
13.8. Exercises 309
13.9. Internet links 310
Conclusion 311
Bibliography 315
Index 327
