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Linear Algebra with Python: Theory and Applications (Springer Undergraduate Texts in Mathematics and Technology)
by Makoto Tsukada Yuji Kobayashi Hiroshi Kaneko Sin-Ei Takahasi Kiyoshi Shirayanagi Masato NoguchiThis textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms. A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron–Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python’s libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi.
Linear and Integer Programming Made Easy
by T. C. Hu Andrew B. KahngThis textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc. , are becoming fundamental toolkits for data and information science and technology. The authors' approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation. Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.
Linear and Mixed Integer Programming for Portfolio Optimization
by Renata Mansini Włodzimierz Ogryczak M. Grazia SperanzaThis book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
Linear and Nonlinear Programming
by David G. Luenberger Yinyu YeThis new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the fourth edition expands and further illustrates this relationship. As in the earlier editions, the material in this fourth edition is organized into three separate parts. Part I is a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. It is possible to go directly into Parts II and III omitting Part I, and, in fact, the book has been used in this way in many universities. New to this edition is a chapter devoted to Conic Linear Programming, a powerful generalization of Linear Programming. Indeed, many conic structures are possible and useful in a variety of applications. It must be recognized, however, that conic linear programming is an advanced topic, requiring special study. Another important topic is an accelerated steepest descent method that exhibits superior convergence properties, and for this reason, has become quite popular. The proof of the convergence property for both standard and accelerated steepest descent methods are presented in Chapter 8. As in previous editions, end-of-chapter exercises appear for all chapters. From the reviews of the Third Edition: ". . . this very well-written book is a classic textbook in Optimization. It should be present in the bookcase of each student, researcher, and specialist from the host of disciplines from which practical optimization applications are drawn. " (Jean-Jacques Strodiot, Zentralblatt MATH, Vol. 1207, 2011)
Linear Mixed-Effects Models Using R
by Tomasz Burzykowski Andrzej GałeckiLinear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.<P> Advisory: Bookshare has learned that this book offers only partial accessibility. We have kept it in the collection because it is useful for some of our members. To explore further access options with us, please contact us through the Book Quality link on the right sidebar. Benetech is actively working on projects to improve accessibility issues such as these.
Linear Models with Python (Chapman & Hall/CRC Texts in Statistical Science)
by Julian J. FarawayPraise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal Throughout, it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.
Linear Network Error Correction Coding
by Xuan Guang Zhen ZhangThere are two main approaches in the theory of network error correction coding. In this SpringerBrief, the authors summarize some of the most important contributions following the classic approach, which represents messages by sequences similar to algebraic coding, and also briefly discuss the main results following the other approach, that uses the theory of rank metric codes for network error correction of representing messages by subspaces. This book starts by establishing the basic linear network error correction (LNEC) model and then characterizes two equivalent descriptions. Distances and weights are defined in order to characterize the discrepancy of these two vectors and to measure the seriousness of errors. Similar to classical error-correcting codes, the authors also apply the minimum distance decoding principle to LNEC codes at each sink node, but use distinct distances. For this decoding principle, it is shown that the minimum distance of a LNEC code at each sink node can fully characterize its error-detecting, error-correcting and erasure-error-correcting capabilities with respect to the sink node. In addition, some important and useful coding bounds in classical coding theory are generalized to linear network error correction coding, including the Hamming bound, the Gilbert-Varshamov bound and the Singleton bound. Several constructive algorithms of LNEC codes are presented, particularly for LNEC MDS codes, along with an analysis of their performance. Random linear network error correction coding is feasible for noncoherent networks with errors. Its performance is investigated by estimating upper bounds on some failure probabilities by analyzing the information transmission and error correction. Finally, the basic theory of subspace codes is introduced including the encoding and decoding principle as well as the channel model, the bounds on subspace codes, code construction and decoding algorithms.
Linear Programming: Second Edition
by Prof. Daniel SolowSuitable for undergraduate students of mathematics and graduate students of operations research and engineering, this text covers the basic theory and computation for a first course in linear programming. In addition to substantial material on mathematical proof techniques and sophisticated computation methods, the treatment features numerous examples and exercises. An introductory chapter offers a systematic and organized approach to problem formulation. Subsequent chapters explore geometric motivation, proof techniques, linear algebra and algebraic steps related to the simplex algorithm, standard phase 1 problems, and computational implementation of the simplex algorithm. Additional topics include duality theory, issues of sensitivity and parametric analysis, techniques for handling bound constraints, and network flow problems. Helpful appendixes conclude the text, including a new addition that explains how to use Excel to solve linear programming problems.
Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science #285)
by Robert J. VanderbeiThe book provides a broad introduction to both the theory and the application of optimization with a special emphasis on the elegance, importance, and usefulness of the parametric self-dual simplex method. The book assumes that a problem in “standard form,” is a problem with inequality constraints and nonnegative variables. The main new innovation to the book is the use of clickable links to the (newly updated) online app to help students do the trivial but tedious arithmetic when solving optimization problems.The latest edition now includes: a discussion of modern Machine Learning applications, as motivational material; a section explaining Gomory Cuts and an application of integer programming to solve Sudoku problems. Readers will discover a host of practical business applications as well as non-business applications. Topics are clearly developed with many numerical examples worked out in detail. Specific examples and concrete algorithms precede more abstract topics. With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, the primal-dual simplex method, the path-following interior-point method, and and the homogeneous self-dual method. In addition, the author provides online tools that illustrate various pivot rules and variants of the simplex method, both for linear programming and for network flows. These C programs and online pivot tools can be found on the book's website. The website also includes new online instructional tools and exercises.
Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management
by Eiji OkiExplaining how to apply to mathematical programming to network design and control, Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management fills the gap between mathematical programming theory and its implementation in communication networks. From the basics all the way through to m
Linear Programming and Economic Analysis (Dover Books on Computer Science)
by Paul A. Samuelson Robert M. Solow Robert DorfmanDesigned primarily for economists and those interested in management economics who are not necessarily accomplished mathematicians, this text offers a clear, concise exposition of the relationship of linear programming to standard economic analysis. The research and writing were supported by The RAND Corporation in the late 1950s.Linear programming has been one of the most important postwar developments in economic theory, but until publication of the present volume, no text offered a comprehensive treatment of the many facets of the relationship of linear programming to traditional economic theory. This book was the first to provide a wide-ranging survey of such important aspects of the topic as the interrelations between the celebrated von Neumann theory of games and linear programming, and the relationship between game theory and the traditional economic theories of duopoly and bilateral monopoly.Modern economists will especially appreciate the treatment of the connection between linear programming and modern welfare economics and the insights that linear programming gives into the determinateness of Walrasian equilibrium. The book also offers an excellent introduction to the important Leontief theory of input-output as well as extensive treatment of the problems of dynamic linear programming. Successfully used for three decades in graduate economics courses, this book stresses practical problems and specifies important concrete applications.
Linear Programming Computation
by Ping-Qi PanWith emphasis on computation, this book is a real breakthrough in the field of LP. In addition to conventional topics, such as the simplex method, duality, and interior-point methods, all deduced in a fresh and clear manner, it introduces the state of the art by highlighting brand-new and advanced results, including efficient pivot rules, Phase-I approaches, reduced simplex methods, deficient-basis methods, face methods, and pivotal interior-point methods. In particular, it covers the determination of the optimal solution set, feasible-point simplex method, decomposition principle for solving large-scale problems, controlled-branch method based on generalized reduced simplex framework for solving integer LP problems.
Linear Programming Using MATLAB® (Springer Optimization and Its Applications #127)
by Nikolaos Ploskas Nikolaos SamarasThis book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB#65533; code. The MATLAB#65533; implementations presented in this book are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus. The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis.
Linear Regression
by David J. OliveThis text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models.This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.
Linear System Theory and Design
by Chi-Tsong ChenAn extensive revision of the author's highly successful text, this third edition of Linear System Theory and Design has been made more accessible to students from all related backgrounds. After introducing the fundamental properties of linear systems, the text discusses design using state equations and transfer functions. In state-space design, Lyapunov equations are used extensively to design state feedback and state estimators. In the discussion of transfer-function design, pole placement, model matching, and their applications in tracking and disturbance rejection are covered. Both one-and two-degree-of-freedom configurations are used. All designs can be accomplished by solving sets of linear algebraic equations. <p><p> All results in this new edition are developed for numerical computation and illustrated using MATLAB, with an emphasis on the ideas behind the computation and interpretation of results. This book develops all theorems and results in a logical way so that readers can gain an intuitive understanding of the theorems. This revised edition begins with the time-invariant case and extends through the time-varying case. It also starts with single-input single-output design and extends to multi-input multi-output design. Striking a balance between theory and applications, Linear System Theory and Design, 3/e, is ideal for use in advanced undergraduate/first-year graduate courses in linear systems and multivariable system design in electrical, mechanical, chemical, and aeronautical engineering departments. It assumes a working knowledge of linear algebra and the Laplace transform and an elementary knowledge of differential equations.
Linear Time Series with MATLAB and OCTAVE (Statistics and Computing)
by Víctor GómezThis book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.
Lineare Kirchhoff-Netzwerke: Grundlagen, Analyse und Synthese
by Reiner ThieleDas Buch vermittelt ausgehend von den Grundlagen der Netzwerk-Theorie neuartige Analyse- und Syntheseverfahren für lineare zeitinvariante Kirchhoff-Netzwerke. Hierzu verwendet der Autor als Elementarnetzwerke gewöhnliche Widerstände, Kondensatoren und Spulen sowie die sogenannten pathologischen Unternetzwerke Nullator, Norator und Nullor. Der Nullor besteht dabei aus einem Nullator und einem Norator, wird hinsichtlich seines Klemmenverhaltens durch die Belevitch-Darstellung beschrieben und näherungsweise durch einen Operationsverstärker realisiert. Zur Analyse oder Synthese erfolgt die Zerlegung in realisierbare Unternetzwerke mit dem Verfahren der Singulärwert-Zerlegung von Matrizen. Außerdemzeigt Reiner Thiele, wie durch die Applikation vonKlemmen-Äquivalenzenpraxisrelevante elektrische oder elektronische Schaltungen entstehen.
Lineare Kirchhoff-Netzwerke: Grundlagen, Analyse und Synthese
by Reiner ThieleDas Buch vermittelt ausgehend von den Grundlagen der Netzwerk-Theorie neuartige Analyse- und Syntheseverfahren für lineare zeitinvariante Kirchhoff-Netzwerke. Hierzu verwendet der Autor als Elementarnetzwerke gewöhnliche Widerstände, Kondensatoren und Spulen sowie die sogenannten pathologischen Unternetzwerke Nullator, Norator und Nullor. Der Nullor besteht dabei aus einem Nullator und einem Norator, wird hinsichtlich seines Klemmenverhaltens durch die Belevitch-Darstellung beschrieben und näherungsweise durch einen Operationsverstärker realisiert. Zur Analyse oder Synthese erfolgt die Zerlegung in realisierbare Unternetzwerke mit dem Verfahren der Singulärwert-Zerlegung von Matrizen. Außerdem zeigt Reiner Thiele, wie durch die Applikation von Klemmen-Äquivalenzen praxisrelevante elektrische oder elektronische Schaltungen entstehen.
Lineare Kirchhoff-Netzwerke: Grundlagen, Analyse und Synthese
by Reiner ThieleDas Buch vermittelt ausgehend von den Grundlagen der Netzwerk-Theorie neuartige Analyse- und Syntheseverfahren für lineare zeitinvariante Kirchhoff-Netzwerke. Hierzu verwendet der Autor als Elementarnetzwerke gewöhnliche Widerstände, Kondensatoren und Spulen sowie die sogenannten pathologischen Unternetzwerke Nullator, Norator und Nullor. Der Nullor besteht dabei aus einem Nullator und einem Norator, wird hinsichtlich seines Klemmenverhaltens durch die Belevitch-Darstellung beschrieben und näherungsweise durch einen Operationsverstärker realisiert. Zur Analyse oder Synthese erfolgt die Zerlegung in realisierbare Unternetzwerke mit dem Verfahren der Singulärwert-Zerlegung von Matrizen. Außerdem zeigt Reiner Thiele, wie durch die Applikation von Klemmen-Äquivalenzen praxisrelevante elektrische oder elektronische Schaltungen entstehen.
Lingo in a Nutshell
by Bruce A. EpsteinMacromedia Director 6 is the premiere authoring tool for delivering interactive content on both the Internet and the desktop. It is the dominant multimedia package for Windows 95/NT, Windows 3.1, and the Macintosh. A quarter million developers use Director(R) to incorporate animation and audio into dynamic Web pages, and to create engaging interactive corporate presentations, multimedia advertising, entertainment CD-ROMs, Enhanced music CDs, and even DVDs. Lingo is Director's powerful scripting language. This companion book to Director in a Nutshell is an essential tool for both new and experienced Lingo programmers seeking a deeper knowledge of the language. Bruce Epstein is the author of both these books and brings years of hands-on experience with Director and Lingo. The book includes numerous useful Lingo examples. Exhaustively tested, this book corrects many errors found in Macromedia's Lingo documentation and repeated verbatim in most third-party books. Extremely comprehensive, this book details dozens of misdocumented and undocumented Lingo keywords that are omitted from Macromedia's manuals and third-party books. Lingo in a Nutshell caters to the huge pool of Director users attempting to bridge the Lingo gap, yet provides the details for the experienced Linguist that are lacking in other Lingo books. In typical nutshell style (clear, concise, deep and narrow) this book explores the syntax, structure and commands of the Lingo language. The detailed chapters describe messages, events, scripts, handlers, variables, lists, file I/O, Behaviors, child objects, Xtras, and more. This book teaches you to troubleshoot and debug common Lingo errors. Lingo in a Nutshell is the book for which both Director users and power Lingo programmers have been yearning. The book extensively covers topics not found in other Lingo books: Cross-platform Lingo differences Lingo internals for experienced programmers Events, messages, and scripts Timers, tempos, cue points, and synchronization Data types and expressions Math, numerical expressions, geometry, and trigonometry Coordinates, alignment, and registration point Lingo in a Nutshell is the most concise and compete guide available. It is a high-end handbook at a low-end price. An essential desktop reference for every Director user.
Linguistic Linked Open Data
by Diana Trandabăţ Daniela GîfuThis book constitutes therefereed proceedings of the 12th EUROLAN Summer School on Linguistic Linked Open Data and its Satellite Workshop on Social Media and the Web of Linked Data, RUMOUR 2015, held in Sibiu, Romania, in July 2015. The 10 revised full papers presented together with 12 abstracts of tutorials werecarefully reviewed and selected from 21 submissions.
Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis (Studies in Fuzziness and Soft Computing #414)
by Mohammad YazdiThis book reviews and presents a number of approaches to Fuzzy-based system safety and reliability assessment. For each proposed approach, it provides case studies demonstrating their applicability, which will enable readers to implement them into their own risk analysis process.The book begins by giving a review of using linguistic terms in system safety and reliability analysis methods and their extension by fuzzy sets. It then progresses in a logical fashion, dedicating a chapter to each approach, including the 2-tuple fuzzy-based linguistic term set approach, fuzzy bow-tie analysis, optimizing the allocation of risk control measures using fuzzy MCDM approach, fuzzy sets theory and human reliability, and emergency decision making fuzzy-expert aided disaster management system.This book will be of interest to professionals and researchers working in the field of system safety and reliability, as well as postgraduate and undergraduate students studying applications of fuzzy systems.
Linguistic Modeling of Information and Markup Languages
by Andreas Witt Dieter MetzingThis book addresses the interests of a large community of researchers in the fields of XML-based annotation techniques and corpus-based language technology. It covers the most significant recent developments in this field, from multi-layered mark-up and standards to theoretical formalisms to applications. The contributions are based on research projects at international level in text technology, computational linguistics, hypertext modeling and in the domain of standards and tools for language resources. Core topics are: strategies for multi-layered document modeling and processing, mark-up at different levels for textual resources, and text-technological information modeling. The sections of the book offer an exhaustive coverage of many of the current topics in the fields concerned, especially: Multi-layered Markup; Markup Languages and Language Resources; Markup and Text Types; Markup Languages and Hypertext; Markup and Formalization.
Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP Software
by Max SilberzteinEmpirical — data-driven, neural network-based, probabilistic, and statistical — methods seem to be the modern trend. Recently, OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars.Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented.
Linguistic Response to the Taboo of Death in Egyptian Arabic
by Magdalena ZawrotnaThe work presents a study of the linguistic and pragmatic response to the taboo of death in Egypt. The analysis leads the author to the conclusion that the experience of death in Egyptian society is mediated by religion. The reaction to death announcements includes a number of strategies to protect both the author of the utterance and its recipient against the effects of the taboo related to this topic. The most important feature of the studied communication is formulaicity, which is at the same time the central idea and the methodological frame of the work presented here.The discourse analyzed here fits within the Arab-Muslim rhetorical framework. In the daily utterances of the Egyptians, divine agency is believed to be constantly present, which is attested in numerous ritual practices. As part of the quantitative study and the structural analysis of the material, a pattern was distinguished in which individual types of formulas occur in their fixed places and a specific order. Qualitatively, many of the statements in the material are strongly emotional. To enhance the pragmatic effect, phrases are combined with each other, repetitions, prayers, poetic attempts and quotes from the Quran/ Hadith appear. Most of the phrases used in response to the taboo of death are prefabricated items recalled from memory almost automatically. Further analysis proposes to look at the formulae in the context of taboo and strong emotions related to it. Using formulaic sequence instead of generating novel language enables. the author of the utterance to convey emotional support to the suffering person and, at the same time, eliminates ambiguity. The methodology proposed here offers a new insight into the language of everyday communication, through the lens of its pragmatic usefulness and linguistic etiquette, taking into account the cultural framework in which the analyzed utterances are performed.