Computational and corpus linguistics Books

Book SynopsisThis technique is an example of one facet of the PLNLP approach: the use of natural language itself as a knowledge representation language -- an innovation that permits a wide variety of online text materials to be exploited as sources of semantic information.Table of Contents1. Introduction; K. Jensen, G. Heidorn, S. Richardson. 2. Towards Transductive Linguistics; A.M. Ramer. 3. PEG: The PLNLP English Grammar; K. Jensen. 4. Experience with an Easily Computed Metric for Ranking Alternative Parses; G. Heidorn. 5. Parse Fitting and Prose Fixing; K. Jensen, G. Heidorn, L. Miller, Y. Ravin. 6. Grammar Errors and Style Weaknesses in a Text-Critiquing System; Y. Ravin. 7. The Experience of Developing a Large-Scale Natural Language Processing System: Critique; S. Richardson, L. Braden-Harder. 8. A Prototype English-Japanese Machine Translation System; T. Tsutsumi. 9. Broad-Coverage Machine Translation; D. Santos. 10. Building a Knowledge Base from Parsed Definitions; J. Klavans, M. Chodorow, N. Wacholder. 11. A Semantic Expert Using an Online Standard Dictionary; J.-L. Binot, K. Jensen. 12. Structural Patterns versus String Patterns for Extracting Semantic Information from Dictionaries; S. Montemagni, L. Vanderwende. 13. SENS: The System for Evaluating Noun Sequences; L. Vanderwende. 14. Disambiguating and Interpreting Verb Definitions; Y. Ravin. 15. Tailoring a Broad-Coverage Systems for the Analysis of Dictionary Definitions; S. Montemagni. 16. PEGASUS: Deriving Argument Structures after Syntax; K. Jensen. 17. A Two-Stage Algorithm to Parse Multi-Lingual Argument Structures; J.-P. Chanod, B. Harriehausen, S. Montemagni. 18. C-SHALT: English-to-Chinese Machine Translation Using Argument Structures; Ee Ah Choo, Koh Mui Koong, Low Hwee Boon, Tong Loong Cheong, Wan Kwee Ngim, Wee Li Kwang. 19. Sense Disambiguation Using Online Dictionaries; L. Braden-Harder. 20. Word-Sense Disambiguation by Examples; T. Tsutsumi. 21. Nominalization of Semantic Graphs; F. Segond. 22. The Paragraph as a Semantic Unit; W. Zadrozny, K. Jensen. References. Index.

Read more less

Book SynopsisFinding a particular scientific document amid a sea of thousands of other documents can often seem like an insurmountable task. "The Structure of Scientific Articles" shows how linguistic theory can provide a solution by analyzing rhetorical structures to make information retrieval easier and faster. Through the use of an improved citation indexing system, this indispensable volume applies empirical discourse studies to pressing issues of document management, including attribution, the author's stance towards other work, and problem-solving processes.

Read more less

Book SynopsisLinguistic Issues in Language Technology focuses on the relationships between linguistic insights and language technology. In conjunction with machine learning and statistical techniques, more sophisticated models of language and speech are needed to make significant progress in both existing and newly emerging areas of computational language analysis. The vast quantity of electronically accessible natural language data provides unprecedented opportunities for data-intensive analysis of linguistic phenomena, which can in turn enrich computational methods. Linguistic Issues in Language Technology provides a forum for this work. In this volume, contributors offer new perspectives on semantic representations for textual inference.

Read more less

Book SynopsisThis volume collects landmark research in a burgeoning field of visual analytics for linguistics, called LingVis. Combining linguistic data and linguistically oriented research questions with techniques and methodologies developed in the computer science fields of visual analytics and information visualization, LingVis is motivated by the growing need within linguistic research for dealing with large amounts of complex, multidimensional data sets. An innovative exploration into the future of LingVis in the digital age, this foundational book both provides a representation of the current state of the field and communicates its new possibilities for addressing complex linguistic questions across the larger linguistic community.

Read more less

Book SynopsisThis book is an introduction to the linguistic concepts of argumentation relevant for argument mining, an important research and development activity which can be viewed as a highly complex form of information retrieval, requiring high-level natural language processing technology. While the first four chapters develop the linguistic and conceptual aspects of argument expression, the last four are devoted to their application to argument mining. These chapters investigate the facets of argument annotation, as well as argument mining system architectures and evaluation. How annotations may be used to develop linguistic data and how to train learning algorithms is outlined. A simple implementation is then proposed. The book ends with an analysis of non-verbal argumentative discourse. Argument Mining is an introductory book for engineers or students of linguistics, artificial intelligence and natural language processing. Most, if not all, the concepts of argumentation crucial for argument mining are carefully introduced and illustrated in a simple manner.Table of ContentsPreface xi Chapter 1. Introduction and Challenges 1 1.1. What is argumentation? 1 1.2. Argumentation and argument mining 4 1.3. The origins of argumentation 7 1.4. The argumentative discourse 8 1.5. Contemporary trends 10 Chapter 2. The Structure of Argumentation 13 2.1. The argument–conclusion pair 13 2.2. The elementary argumentative schema 14 2.2.1. Toulmin’s argumentative model 14 2.2.2. Some elaborations and refinements of Toulmin’s model 17 2.2.3. The geometry of arguments 18 2.3. Modeling agreement and disagreement 20 2.3.1. Agreeing versus disagreeing 20 2.3.2. The art of resolving divergences 23 2.4. The structure of an argumentation: argumentation graphs 25 2.5. The role of argument schemes in argumentation 27 2.5.1. Argument schemes: main concepts 27 2.5.2. A few simple illustrations 28 2.5.3. Argument schemes based on analogy 29 2.5.4. Argument schemes based on causality 30 2.6. Relations between Toulmin’s model and argumentation schemes 31 2.6.1. Warrants as a popular opinion 32 2.6.2. Argument schemes based on rules, explanations or hypothesis 34 2.6.3. Argument schemes based on multiple supports or attacks 35 2.6.4. Causality and warrants 37 Chapter 3. The Linguistics of Argumentation 39 3.1. The structure of claims 40 3.2. The linguistics of justifications 45 3.3. Evaluating the strength of claims, justifications and arguments 47 3.3.1. Strength factors within a proposition 49 3.3.2. Structuring expressions of strength by semantic category 51 3.3.3. A simple representation of strength when combining several factors 52 3.3.4. Pragmatic factors of strength expression 53 3.4. Rhetoric and argumentation 59 3.4.1. Rhetoric and communication 60 3.4.2. Logos: the art of reasoning and of constructing demonstrations 61 3.4.3. Ethos: the orator profile 62 3.4.4. Pathos: how to persuade an audience 63 Chapter 4. Advanced Features of Argumentation for Argument Mining 65 4.1. Managing incoherent claims and justifications 65 4.1.1. The case of justifications supporting opposite claims 66 4.1.2. The case of opposite justifications justifying the same claim 67 4.2. Relating claims and justifications: the need for knowledge and reasoning 67 4.2.1. Investigating relatedness via corpus analysis 68 4.2.2. A corpus analysis of the knowledge involved 69 4.2.3. Observation synthesis 72 4.3. Argument synthesis in natural language 74 4.3.1. Features of a synthesis 75 4.3.2. Structure of an argumentation synthesis 76 Chapter 5. From Argumentation to Argument Mining 79 5.1. Some facets of argument mining 79 5.2. Designing annotation guidelines: some methodological elements 81 5.3. What results can be expected from an argument mining system? 82 5.4. Architecture of an argument mining system 83 5.5. The next chapters 84 Chapter 6. Annotation Frameworks and Principles of Argument Analysis 85 6.1. Principles of argument analysis 86 6.1.1. Argumentative discourse units 86 6.1.2. Conclusions and premises 88 6.1.3. Warrants and backings 89 6.1.4. Qualifiers 89 6.1.5. Argument schemes 90 6.1.6. Attack relations: rebuttals, refutations, undercutters 90 6.1.7. Illocutionary forces, speech acts 92 6.1.8. Argument relations 93 6.1.9. Implicit argument components and tailored annotation frameworks 95 6.2. Examples of argument analysis frameworks 97 6.2.1. Rhetorical Structure Theory 97 6.2.2. Toulmin’s model 98 6.2.3. Inference Anchoring Theory 99 6.2.4. Summary 102 6.3. Guidelines for argument analysis 103 6.3.1. Principles of annotation guidelines 103 6.3.2. Inter-annotator agreements 104 6.3.3. Interpretation of IAA measures 105 6.3.4. Some examples of IAAs 106 6.3.5. Summary 107 6.4. Annotation tools 108 6.4.1. Brat 108 6.4.2. RST tool 109 6.4.3. AGORA-net 110 6.4.4. Araucaria 110 6.4.5. Rationale 111 6.4.6. OVA+ 112 6.4.7. Summary 113 6.5. Argument corpora 114 6.5.1. COMARG 115 6.5.2. A news editorial corpus 115 6.5.3. THF Airport ArgMining corpus 115 6.5.4. A Wikipedia articles corpus 115 6.5.5. AraucariaDB 115 6.5.6. An annotated essays corpus 116 6.5.7. A written dialogs corpus 116 6.5.8. A web discourse corpus 116 6.5.9. Argument Interchange Format Database 116 6.5.10. Summary 117 6.6. Conclusion 118 Chapter 7. Argument Mining Applications and Systems 119 7.1. Application domains for argument mining 119 7.1.1. Opinion analysis augmented by argument mining 120 7.1.2. Summarization 120 7.1.3. Essays 120 7.1.4. Dialogues 120 7.1.5. Scientific and news articles 120 7.1.6. The web 121 7.1.7. Legal field 121 7.1.8. Medical field 121 7.1.9. Education 121 7.2. Principles of argument mining systems 122 7.2.1. Argumentative discourse units detection 123 7.2.2. Units labeling 123 7.2.3. Argument structure detection 124 7.2.4. Argument completion 125 7.2.5. Argument structure representation 125 7.3. Some existing systems for argument mining 126 7.3.1. Automatic detection of rhetorical relations 126 7.3.2. Argument zoning 126 7.3.3. Stance detection 127 7.3.4. Argument mining for persuasive essays 127 7.3.5. Argument mining for web discourse 127 7.3.6. Argument mining for social media 128 7.3.7. Argument scheme classification and enthymemes reconstruction 128 7.3.8. Argument classes and argument strength classification 128 7.3.9. Textcoop 129 7.3.10. IBM debating technologies 129 7.3.11. Argument mining for legal texts 129 7.4. Efficiency and limitations of existing argument mining systems 130 7.5. Conclusion 131 Chapter 8. A Computational Model and a Simple Grammar-Based Implementation 133 8.1. Identification of argumentative units 134 8.1.1. Challenges raised by the identification of argumentative units 134 8.1.2. Some linguistic techniques to identify ADUs 135 8.2. Mining for claims 139 8.2.1. The grammar formalisms 140 8.2.2. Lexical issues 142 8.2.3. Grammatical issues 145 8.2.4. Templates for claim analysis 148 8.3. Mining for supports and attacks 150 8.3.1. Structures introduced by connectors 150 8.3.2. Structures introduced by propositional attitudes 151 8.3.3. Other linguistic forms to express supports or attacks 152 8.4. Evaluating strength 153 8.5. Epilogue 154 Chapter 9. Non-Verbal Dimensions of Argumentation: a Challenge for Argument Mining 155 9.1. The text and its additions 156 9.1.1. Text, pictures and icons 156 9.1.2. Transcriptions of oral debates 156 9.2. Argumentation and visual aspects 157 9.3. Argumentation and sound aspects 158 9.3.1. Music and rationality 159 9.3.2. Main features of musical structure: musical knowledge representation 160 9.4. Impact of non-verbal aspects on argument strength and on argument schemes 161 9.5. Ethical aspects 162 Bibliography 163 Index 175

Read more less

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, CroatiaTable 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

Read more less

Book SynopsisWhat are multimodal texts? How can we transcribe and analyse them? How can multimedia and internet help us in multimodal discourse analysis? What postproduction and authoring skills are needed to analyse a multimodal text or to develop a corpus of multimodal texts? How does integrating multimedia meaning-making resources into hypertext multiply our meaning-making potential? How does the study of language relate to multimodality and multimedia, in particular in the e-learning age? How, and to what extent, will multimodal discourse analysis re-shape linguistics? In its attempt to provide answers to the questions raised above, and many others, this book proposes concrete solutions to the problems of multimodal text analysis and transcription of printed texts, websites and film. As such, it constitutes a much needed course in multimodal text transcription and analysis. It also suggests ways in which multimodal discourse analysis can help both educators and students understand how meaning is made in the e-learning environments that now play such an important role in our lives. In both these respects, readers are encouraged to use the book in conjunction with an associated and freely accessible website which provides many illustrations and exercises that further contextualise and exemplify the insights and descriptions provided by the book. As befits a coursebook, the individual chapters of the book are carefully organised in such a way as to provide a step-by-step progression in theoretical and descriptive complexity.

Read more less

Book SynopsisThis case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of datasets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide applicability. Fields featured include, but are not limited to, historical agriculture, arts (music and painting), theology, and stylometrics (authorship issues). Most analyses are based on existing data, earlier analysed in published peer-reviewed papers.Four preliminary methodological and statistical chapters provide general technical background to the case studies. The multivariate statistical methods presented and illustrated include data inspection, several varieties of principal component analysis, correspondence analysis, multidimensional scaling, cluster analysis, regression analysis, discriminant analysis, and three-mode analysis.The bulk of the text is taken up by 14 case studies that lean heavily on graphical representations of statistical information such as biplots, using descriptive statistical techniques to support substantive conclusions. Each study features a description of the substantive background to the data, followed by discussion of appropriate multivariate techniques, and detailed results interpreted through graphical illustrations. Each study is concluded with a conceptual summary. Datasets in SPSS are included online.Table of Contents

Read more less

Book SynopsisArgumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.Table of ContentsPreface.- Acknowledgments.- Introduction.- Argumentative Language.- Modeling Arguments.- Corpus Annotation.- Finding Claims.- Finding Supporting and Objecting Statements.- Deriving the Structure of Argumentation.- Assessing Argumentation.- Generating Argumentative Text.- Summary and Perspectives.- Bibliography.- Authors' Biographies.- Index.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book SynopsisNatural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented.This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects.Trade Review"This book does a phenomenal job capturing the real-world techniques employed by industry experts to address complex problems with remarkable finesse and effectiveness. From foundational techniques to cutting-edge models, this book seamlessly blends practical code examples and insightful applications to provide a comprehensive understanding. Whether you're a novice or an experienced practitioner, this book will take you on a journey through the entire NLP landscape, providing the knowledge and skills needed to tackle any linguistic challenge and enhance your grasp of NLP. " - Sumanik Singh, Software Engineer at Amazon (Alexa Smart Home)"This book does an exceptional job of covering a wide range of NLP applications, making it a must-read for anyone interested in understanding the potential of this rapidly evolving field. It introduces the fundamental concepts of NLP in a clear and concise manner, ensuring that readers without a strong technical background can grasp the subject matter. It then delves deeper into advanced techniques and algorithms, providing readers with the necessary tools to implement NLP solutions effectively." - Neha Tiwari, Senior Data Scientist at Nielsen"This book does a phenomenal job capturing the real-world techniques employed by industry experts to address complex problems with remarkable finesse and effectiveness. From foundational techniques to cutting-edge models, this book seamlessly blends practical code examples and insightful applications to provide a comprehensive understanding. Whether you're a novice or an experienced practitioner, this book will take you on a journey through the entire NLP landscape, providing the knowledge and skills needed to tackle any linguistic challenge and enhance your grasp of NLP. " - Sumanik Singh, Software Engineer at Amazon (Alexa Smart Home)"This book does an exceptional job of covering a wide range of NLP applications, making it a must-read for anyone interested in understanding the potential of this rapidly evolving field. It introduces the fundamental concepts of NLP in a clear and concise manner, ensuring that readers without a strong technical background can grasp the subject matter. It then delves deeper into advanced techniques and algorithms, providing readers with the necessary tools to implement NLP solutions effectively." - Neha Tiwari, Senior Data Scientist at Nielsen"Often there is a gap between Education and Practice. This book is an essential resource to cover the gap and must-have for beginners as well as experienced professionals. As a researcher in academia and professor of machine learning, I find this book to be an eye opener for approaching NLP in a practical sense. Not only a great resource of people in academia, it is all you need to build NLP solutions in the real world regardless of the industry vertical you work in." - Dr. V. Kalaichelvi, Professor and Head, Department of EEE, Birla Institute of Technology & Science (BITS)"If you’re stuck before you even start your NLP project, this book is just what you need. From key data storage tools for text, to visualization techniques that make sense with language data, to practical use cases in many verticals, "Natural Language Processing in the Real World" will serve as your map, trailguide, and companion on your journey from fresh text dataset to prototype NLP app." - Rebecca Bilbro, Ph.D, Founder and CTO at Rotational Labs, Applied Text Analytics book author, Data Science faculty at Georgetown University"Natural Language Processing in the Real-World is a praiseworthy book that tackles a highly important subject. It provides an accurate representation of real-world applications and solutions, effectively bridging the gap between theory and practice. By exploring NLP across 15 different industry verticals, this book offers readers a comprehensive understanding of how NLP is implemented in practical scenarios. The inclusion of Python code for implementing NLP applications further enhances its worth, as it allows readers to apply their theoretical knowledge to real-world projects. As a researcher and academician, I consider this book to be an invaluable resource, and I believe it holds immense value for my students who are pursuing degrees in ML-related subjects and aspire to build careers in Data Science." - Vwani P. Roychowdhury, Professor, University of California, Los Angeles (UCLA)"The true value of natural language processing lies in its ability to quickly solve real-world business problems. While theory is important, it is the practical application of NLP and its connection to a company's mission that drives meaningful innovation and impact. This book provides a practical playbook, offering insights and techniques that bridge the gap between theory and practice. Data Science often carries the perception of being methodical and slow, but this book focuses on leveraging prototyping, stakeholder interaction, and iteration to integrate data science into the core of value delivery for companies. Whether you're an experienced practitioner or new to the field, this book empowers you to harness the power of NLP and transform the way we interact with language in the real world."- Joey McCord, Founder, CTO and Adjunct Professor"This exceptional guide offers profound insights into the various industries that leverage NLP, its alignment with business objectives, and practical guidance on developing Python-based applications. Whether you are a novice or an expert, this book is an invaluable resource. It comprehensively covers essential knowledge and has become an indispensable tool for enhancing my expertise and proficiency in NLP. I consider it an immensely valuable asset and a frequent reference in my work." - Dishant Banga, Sr. Data Analyst , BridgetreeTable of ContentsTable of Contents:List of FiguresList of TablesContributorsPrefaceAcknowledgements Chapter 1: NLP BasicsChapter 2: Data Sources and ExtractionChapter 3: Data Preprocessing and TransformationChapter 4: Data ModelingChapter 5: NLP Applications – Active UsageChapter 6: NLP Applications – Developing UsageChapter 7: Information Extraction and Text Transforming ModelsChapter 8: Text Categorisation and AffinitiesChapter 9: ChatbotsChapter 10: Customer Review AnalysisChapter 11: Recommendations and PredictionsChapter 12: More Real-World Scenarios and TipsBibliographyIndex

Read more less

Book SynopsisThis book enables readers to interrogate the technical, rhetorical, theoretical, and socio-ethical challenges and opportunities involved in the development and adoption of augmentation technologies and artificial intelligence. The core of our human experience and identity is forever affected by the rise of augmentation technologies that enhance human capability or productivity. These technologies can add cognitive, physical, sensory, and emotional enhancements to the body or environment. This book demonstrates the benefits, risks, and relevance of emerging augmentation technologies such as braincomputer interaction devices for cognitive enhancement; robots marketed to improve human social interaction; wearables that extend human senses, augment creative abilities, or overcome physical limitations; implantables that amplify intelligence or memory; and devices, AI generators, or algorithms for emotional augmentation. It allows scholars and professionals to understand the impactTrade Review"Augmentation Technologies and Artificial Intelligence in Technical Communication: Designing Ethical Futures is a must-read for every technical communication professional who anticipates working alongside or communicating about augmentation technologies and artificial intelligence. Drawing on their extensive research and project experience with these technologies, co-authors Ann Hill Duin and Isabel Pedersen guide researchers, instructors and practicing professionals in the field through this emerging technology ecosystem, raise awareness of the affordances, benefits, and ethical dilemmas posed by these technologies, and explore specific applications of augmentation technologies and AI in the work of technical communicators, in teaching, and in influencing the future direction of the field. This is an ideal book for a textbook or for a study group on these technologies." — Saul Carliner, Concordia University, Canada."With this book, Duin and Pedersen provide an essential resource for those who work and study in the many areas of augmentation technology (AT) and artificial intelligence (AI). They bring technical & professional communication (TPC) firmly into this field. The authors have organized the rich landscape of resources in AT and AI to date—taxonomies, standards, policies, definitions, examples, and applications. They include ethical elements that are also critical to that landscape—human rights, accountability, security, safety, transparency, and explainability. In other words, this book is a repository of resources and a touchstone for future work in AT and AI . . . for TPC professionals as well as others working in the field." — Dr. Pam Estes Brewer, Mercer University, USA.Table of ContentsSection 1: Understand (rhetorics of) Augmentation Technologies 1. Augmentation Technologies and AI – An Ethical Design Futures Framework 2. Dimensions, Scope, and Classification for Augmentation Technologies 3. Agency, Affordances, and Enculturation of Augmentation Technologies Section 2: Build Literacies 4. Competencies, Design Considerations, and New Roles for Work with Augmentation Technologies and AI 5. Socio-ethical Consequences and Design Futures Section 3: Design Ethical Futures 6. Pedagogical Direction for Cultivating Augmentation Technology and AI Literacies 7. Professional Direction for Human-AI Interaction 8. Strategic and Tactical Approaches to Designing Ethical Futures for Augmentation Technologies and AI

Read more less

Book SynopsisApplying Natural Language Processing (NLP) concepts to help humans in their daily life, this book discusses an automatic translation of an unstructured Natural Language Question (NLQ) into a Structured Query Language (SQL) statement. Using SQL as a Relational DataBase (RDB) interaction language, database administrators or general users with little to no SQL querying abilities are provided with all the knowledge necessary to perform queries on RDBs in an interactive manner.Key Features: Includes extensive and illustrative examples to simplify the discussed concepts Discusses a novel, and yet simple, approach to NLP Introduces a lightweight NLQ into SQL translation approach through the use of RDB MetaTables as a Hash table Extensive literature review and thorough background information on every tool, concept and technique applied Providing a unique approach to NLQ into SQTable of ContentsPreface. 1 Introduction. 2 Background Study. 3 Literature Review. 4 Implementation Plan. 5 Implementation User Case Scenario. 6 Implementation Testing and Performance Measurements. 7 Implementation Results Discussion. 8 Conclusion and Future Work. Appendix 1. Appendix 2. Appendix 3. Appendix 4. Appendix 5. Appendix 6. Appendix 7. Appendix 8. Appendix 9. Glossary. References. Index.

Read more less

Book SynopsisHaving been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time.Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitioners Illustrated and organized in an accessible manner, this textbook contains particular appeal to st

Read more less

Book SynopsisFrom tech giants to plucky startups, the world is full of companies boasting that they are on their way to replacing human interpreters, but are they right? Interpreters vs Machines offers a solid introduction to recent theory and research on human and machine interpreting, and then invites the reader to explore the future of interpreting. With a foreword by Dr Henry Liu, the 13th International Federation of Translators (FIT) President, and written by consultant interpreter and researcher Jonathan Downie, this book offers a unique combination of research and practical insight into the field of interpreting.Written in an innovative, accessible style with humorous touches and real-life case studies, this book is structured around the metaphor of playing and winning a computer game. It takes interpreters of all experience levels on a journey to better understand their own work, learn how computers attempt to interpret and explore possible futures for human interpreters. <Trade ReviewJonathan Downie continues his mission to bring interpreting research to the people. Outspokenly, he tackles fundamental questions for interpreters in the 21st Century. Firmly grounded in Interpreting Studies, Downie interlaces research with anecdotes well-founded in any interpreter’s daily life. It is an equally trailblazing and sulphurous book on the aspirations of machine interpreting, and the fatal mistake of not making a difference. The book is a welcome addition both to the debate on the future of interpreting and to my students’ literature list. Elisabet Tiselius, Stockholm University, SwedenA deep exploration of the limits of language, technology and the enabling power of human mediation in promoting understanding. This book puts interpreters back in the driver's seat, where they belong.Ewandro Magalhaes, Technology Advocate and Former Chief Interpreter in the UN System, USATable of ContentsIntroductionLevel One – The fundamentalsChapter 1: What is interpreting?Chapter 2: How humans interpretChapter 3: How computers "interpret"Level Two – How machines gained the upper handChapter 4: How we wrecked our own PRChapter 5: Speech translation's marvellous (but misleading) marketing Level Three – Choose your interpreting futureChapter 6: Human interpreting as a stopgapChapter 7: Hanging on with legal help Chapter 8: Mastering niches Chapter 9: Making interpreting matter againLevel Four – Interpreting that beats the botsChapter 10: Beating the bots Stage One: taking back interpreting PRChapter 11: Marketing interpreting that mattersChapter 12: Deliver more than wordsChapter 13: Coaching and supervisionLevel Five – One last thoughtChapter 14: It's time to call a truceBibliographyIndex

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book SynopsisThis 1992 collection takes the exciting step of examining natural language phenomena from the perspective of both computational linguistics and formal semantics. Computational linguistics has until now been primarily concerned with the construction of computational models for handling the complexities of linguistic form, but has not tackled the questions of representing or computing meaning. Formal semantics, on the other hand, has attempted to account for the relations between forms and meanings, without necessarily attending to computational concerns. The book introduces the reader to the two disciplines and considers the prospects for the more unified and comprehensive computational theory of language which might obtain from their amalgamation. Of great interest to those working in the fields of computation, logic, semantics, artificial intelligence and linguistics generally.Trade Review"...makes important theoretical progress in the formalism of natural language processing. The accent is on the relations between the syntax and the semantics of natural language. Also, the commonalities of natural language and artificial intelligence are stressed....readers will find the state of the art in the theory of natural language processing and some important new contributions." Claudiu Popescu, Computing Reviews"This is a collection of excellent papers....The overall high quality of the contributions should make it valuable to all computational linguists interested in semantics." John Nerbonne, Computational LinguisticsTable of Contents1. Unification; 2. Representations and interpretations; 3. Syntactic categories and semantic type; 4. Fine-structure in categorical semantics; 5. Properties, propositions and semantic theory; 6. Algorithms for semantic interpretation; 7. Situation schemata and linguistic representation; 8. Application-oriented computational semantics; 9. Form and content in semantics; Epilogue: on the relation between computational linguistics and formal semantics; Bibliography.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less