This is the very first book to investigate the field of phraseology from a learner corpus perspective, bringing together studies at the cutting edge of corpus-based research into phraseology and language learners. The chapters include learner-corpus-based studies of phraseological units in varieties of learner language differentiated in terms of task and/or learner variables, compared with each other or with one or more reference corpora; mixed-methods studies that combine learner corpus data with more experimental data types (e.g. eyetracking); and instruction-oriented studies that show how learner-corpus-based insights can be used to inform second language (L2) teaching and testing. The detailed analysis of a wide range of multiword units (collocations, lexical bundles, lexico-grammatical patterns) and extensive learner corpus data provide the reader with a comprehensive theoretical, methodological and applied perspective onto L2 use in a wide range of situations. The knowledge gained from these learner corpus studies has major implications for L2 theory and practice and will help to inform pedagogical assessment and practice.

Recent decades of studies have been human-centred while zooming in on cognition, verbal choices and performance. (...) [and] have provided interesting results, but which often veer towards quantity rather than quality findings. The new reality, however, requires new directions that move towards a humanism that is rooted in holism, stressing that a living organism needs to refocus in order to see the self as a part of a vast ecosystem. Dr Izabela Dixon, Koszalin University of Technology, Poland This volume is a collection of eight chapters by different authors focusing on ecolinguistics. It is preceded by a preface (..) underlin[ing] the presence of ecolinguistics as a newly-born linguistic theory and practice, something that explains the mosaic of content and method in the various chapters, with a more coherent approach being the aim for future research. Prof. Harald Ulland, Bergen University, Norway

These two volumes consisting of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words." In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the language game." "The concept game is a concept with blurred edges." In the first phrase, "the language game" implies the everyday human activity with language, and in the latter, "game" simply implies an ordinary word. Thus, Wittgenstein precisely stated that a word is fuzzy in real life. Unfortunately this idea about a word was not accepted in the conventional science. We had to wait for Zadeh's fuzzy sets theory. Remembering Wittgenstein's statement, we should consider, on the one hand, the concept of "computing with words" from a philosophical point of view. It deeply relates to the everyday use of a word in which the meaning of a word is fuzzy in its nature.

Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.

Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

This book provides an overview of how comparable corpora can be used to overcome the lack of parallel resources when building machine translation systems for under-resourced languages and domains. It presents a wealth of methods and open tools for building comparable corpora from the Web, evaluating comparability and extracting parallel data that can be used for the machine translation task. It is divided into several sections, each covering a specific task such as building, processing, and using comparable corpora, focusing particularly on under-resourced language pairs and domains. The book is intended for anyone interested in data-driven machine translation for under-resourced languages and domains, especially for developers of machine translation systems, computational linguists and language workers. It offers a valuable resource for specialists and students in natural language processing, machine translation, corpus linguistics and computer-assisted translation, and promotes the broader use of comparable corpora in natural language processing and computational linguistics.

Parsing technology is concerned with finding syntactic structure in language. In parsing we have to deal with incomplete and not necessarily accurate formal descriptions of natural languages. Robustness and efficiency are among the main issuesin parsing. Corpora can be used to obtain frequency information about language use. This allows probabilistic parsing, an approach that aims at both robustness and efficiency increase. Approximation techniques, to be applied at the level of language description, parsing strategy, and syntactic representation, have the same objective. Approximation at the level of syntactic representation is also known as underspecification, a traditional technique to deal with syntactic ambiguity. In this book new parsing technologies are collected that aim at attacking the problems of robustness and efficiency by exactly these techniques: the design of probabilistic grammars and efficient probabilistic parsing algorithms, approximation techniques applied to grammars and parsers to increase parsing efficiency, and techniques for underspecification and the integration of semantic information in the syntactic analysis to deal with massive ambiguity. The book gives a state-of-the-art overview of current research and development in parsing technologies. In its chapters we see how probabilistic methods have entered the toolbox of computational linguistics in order to be applied in both parsing theory and parsing practice. The book is both a unique reference for researchers and an introduction to the field for interested graduate students.

These two volumes consIstmg of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words." In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the language game." "The concept game is a concept with blurred edges." In the first phrase, "the language game" implies the everyday human activity with language, and in the latter, "game" simply implies an ordinary word. Thus, Wittgenstein precisely stated that a word is fuzzy in real life. Unfortunately this idea about a word was not accepted in the conventional science. We had to wait for Zadeh's fuzzy sets theory. Remembering Wittgenstein's statement, we should consider, on the one hand, the concept of "computing with words" from a philosophical point of view. It deeply relates to the everyday use of a word in which the meaning of a word is fuzzy in its nature.

This book presents established and state-of-the-art methods in Language Technology (including text mining, corpus linguistics, computational linguistics, and natural language processing), and demonstrates how they can be applied by humanities scholars working with textual data. The landscape of humanities research has recently changed thanks to the proliferation of big data and large textual collections such as Google Books, Early English Books Online, and Project Gutenberg. These resources have yet to be fully explored by new generations of scholars, and the authors argue that Language Technology has a key role to play in the exploration of large-scale textual data. The authors use a series of illustrative examples from various humanistic disciplines (mainly but not exclusively from History, Classics, and Literary Studies) to demonstrate basic and more complex use-case scenarios. This book will be useful to graduate students and researchers in humanistic disciplines working with textual data, including History, Modern Languages, Literary studies, Classics, and Linguistics. This is also a very useful book for anyone teaching or learning Digital Humanities and interested in the basic concepts from computational linguistics, corpus linguistics, and natural language processing.

At present, Web 2.0 technologies are making traditional research genres evolve and form complex genre assemblage with other genres online. This book takes the perspective of genre analysis to provide a timely examination of professional and public communication of science. It gives an updated overview on the increasing diversification of genres for communicating scientific research today by reviewing relevant theories that contribute an understanding of genre evolution and innovation in Web 2.0. The book also offers a much-needed critical enquiry into the dynamics of languages for academic and research communication and reflects on current language-related issues such as academic Englishes, ELF lects, translanguaging, polylanguaging and the multilingualisation of science. Additionally, it complements the critical reflections with data from small-scale specialised corpora and exploratory survey research. The book also includes pedagogical orientations for teaching/training researchers in the STEMM disciplines and proposes several avenues for future enquiry into research genres across languages.

The book gives a comprehensive discussion of Database Semantics (DBS) as an agent-based data-driven theory of how natural language communication essentially works. In language communication, agents switch between speak mode, driven by cognition-internal content (input) resulting in cognition-external raw data (e.g. sound waves or pixels, which have no meaning or grammatical properties but can be measured by natural science), and hear mode, driven by the raw data produced by the speaker resulting in cognition-internal content. The motivation is to compare two approaches for an ontology of communication: agent-based data-driven vs. sign-based substitution-driven. Agent-based means: design of a cognitive agent with (i) an interface component for converting raw data into cognitive content (recognition) and converting cognitive content into raw data (action), (ii) an on-board, content-addressable memory (database) for the storage and content retrieval, (iii) separate treatments of the speak and the hear mode. Data-driven means: (a) mapping a cognitive content as input to the speak-mode into a language-dependent surface as output, (b) mapping a surface as input to the hear-mode into a cognitive content as output. Oppositely, sign-based means: no distinction between speak and hear mode, whereas substitution-driven means: using a single start symbol as input for generating infinitely many outputs, based on substitutions by rewrite rules. Collecting recent research of the author, this beautiful, novel and original exposition begins with an introduction to DBS, makes a linguistic detour on subject/predicate gapping and slot-filler repetition, and moves on to discuss computational pragmatics, inference and cognition, grammatical disambiguation and other related topics. The book is mostly addressed to experts working in the field of computational linguistics, as well as to enthusiasts interested in the history and early development of this subject, starting with the pre-computational foundations of theoretical computer science and symbolic logic in the 30s.

This book is an excellent introduction to multiword expressions. It provides a unique, comprehensive and up-to-date overview of this exciting topic in computational linguistics. The first part describes the diversity and richness of multiword expressions, including many examples in several languages. These constructions are not only complex and arbitrary, but also much more frequent than one would guess, making them a real nightmare for natural language processing applications. The second part introduces a new generic framework for automatic acquisition of multiword expressions from texts. Furthermore, it describes the accompanying free software tool, the mwetoolkit, which comes in handy when looking for expressions in texts (regardless of the language). Evaluation is greatly emphasized, underlining the fact that results depend on parameters like corpus size, language, MWE type, etc. The last part contains solid experimental results and evaluates the mwetoolkit, demonstrating its usefulness for computer-assisted lexicography and machine translation. This is the first book to cover the whole pipeline of multiword expression acquisition in a single volume. It is addresses the needs of students and researchers in computational and theoretical linguistics, cognitive sciences, artificial intelligence and computer science. Its good balance between computational and linguistic views make it the perfect starting point for anyone interested in multiword expressions, language and text processing in general.

"The Yearbook of Corpus Linguistics and Pragmatics" addresses the interface between the two disciplines and offers a platform to scholars who combine both methodologies to present rigorous and interdisciplinary findings about language in real use. Corpus linguistics and Pragmatics have traditionally represented two paths of scientific thought, parallel but often mutually exclusive and excluding. Corpus Linguistics can offer a meticulous methodology based on mathematics and statistics, while Pragmatics is characterized by its effort in the interpretation of intended meaning in real language. This series will give readers insight into how pragmatics can be used to explain real corpus data and also, how corpora can illustrate pragmatic intuitions.

The present volume, "Yearbook of Corpus Linguistics and Pragmatics 2014: New Empirical and Theoretical Paradigms in Corpus Pragmatics, " proposes innovative research models in the liaison between pragmatics and corpus linguistics to explain language in current cultural and social contexts.

The Natural Language for Artificial Intelligence presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind. The book explains linguistic functioning in the dynamic process of human cognition when forming meaning. After that, an approach to artificial intelligence (AI) is outlined, which works with a more restricted concept of natural language that leads to flaws and ambiguities. Subsequently, the characteristics of natural language and patterns of how it behaves in different branches of science are revealed to indicate ways to improve the development of AI in specific fields of science. A brief description of the universal structure of language is also presented as an algorithmic model to be followed in the development of AI. Since AI aims to imitate the process of the human mind, the book shows how the cross-fertilization between natural language and AI should be done using the logical-axiomatic structure of natural language adjusted to the logical-mathematical processes of the machine.

This book provides a refreshing perspective on the description, study and representation of consonant clusters in Polish. What are the sources of phonotactic complexity? What properties or principles motivate the phonological structure of initial and final consonant clusters? In answering these questions, a necessary turning point consists in investigating sequences of consonants at their most basic level, namely in terms of phonological features. The analysis is exploratory: it leads to discovering prevalent feature patterns in clusters from which new phonotactic generalizations are derived. A recurring theme in the book is that phonological features vary in weight depending on (1) their distribution in a cluster, (2) their position in a word, and (3) language domain. Positional feature weight reflects the relative importance of place, manner and voice features (e.g. coronal, dorsal, strident, continuant) in constructing cluster inventories, minimizing cognitive effort, facilitating production and triggering specific casual speech processes. Feature weights give rise to previously unidentified positional preferences. Rankings of features and preferences are a testing ground for principles of sonority, contrast, clarity of perception and ease of articulation. This volume addresses practitioners in the field seeking new methods of phonotactic modelling and approaches to complexity, as well as students interested in an overview of current research directions in the study of consonant clusters. Sequences of consonants in Polish are certainly among the most remarkable ones that readers will ever encounter in their linguistic explorations. In this volume, they will come to realise that hundreds of unusually long, odd-looking, sonority-violating, morphologically complex and infrequent clusters are in fact well-motivated and structured according to well-defined tactic patterns of features.

A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn "how to chat". Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees

This book presents recent advances in NLP and speech technology, a topic attracting increasing interest in a variety of fields through its myriad applications, such as the demand for speech guided touchless technology during the Covid-19 pandemic. The authors present results of recent experimental research that provides contributions and solutions to different issues related to speech technology and speech in industry. Technologies include natural language processing, automatic speech recognition (for under-resourced dialects) and speech synthesis that are useful for applications such as intelligent virtual assistants, among others. Applications cover areas such as sentiment analysis and opinion mining, Arabic named entity recognition, and language modelling. This book is relevant for anyone interested in the latest in language and speech technology.

Parsing the Turing Test is a landmark exploration of both the philosophical and methodological issues surrounding the search for true artificial intelligence. Will computers and robots ever think and communicate the way humans do? When a computer crosses the threshold into self-consciousness, will it immediately jump into the Internet and create a World Mind? Will intelligent computers someday recognize the rather doubtful intelligence of human beings? Distinguished psychologists, computer scientists, philosophers, and programmers from around the world debate these weighty issues a " and, in effect, the future of the human race a " in this important volume.

This handbook provides a comprehensive account of current research on the finite-state morphology of Georgian and enables the reader to enter quickly into Georgian morphosyntax and its computational processing. It combines linguistic analysis with application of finite-state technology to processing of the language. The book opens with the author's synoptic overview of the main lines of research, covers the properties of the word and its components, then moves up to the description of Georgian morphosyntax and the morphological analyzer and generator of Georgian.The book comprises three chapters and accompanying appendices. The aim of the first chapter is to describe the morphosyntactic structure of Georgian, focusing on differences between Old and Modern Georgian. The second chapter focuses on the application of finite-state technology to the processing of Georgian and on the compilation of a tokenizer, a morphological analyzer and a generator for Georgian. The third chapter discusses the testing and evaluation of the analyzer's output and the compilation of the Georgian Language Corpus (GLC), which is now accessible online and freely available to the research community.Since the development of the analyzer, the field of computational linguistics has advanced in several ways, but the majority of new approaches to language processing has not been tested on Georgian. So, the organization of the book makes it easier to handle new developments from both a theoretical and practical viewpoint.The book includes a detailed index and references as well as the full list of morphosyntactic tags. It will be of interest and practical use to a wide range of linguists and advanced students interested in Georgian morphosyntax generally as well as to researchers working in the field of computational linguistics and focusing on how languages with complicated morphosyntax can be handled through finite-state approaches.

The contributions to this volume are drawn from the interdisciplinary research c- ried out within the Sonderforschungsbereich (SFB 378), a special long-term funding scheme of the German National Science Foundation (DFG). Sonderforschungsbe- ich 378 was situated at Saarland University, with colleagues from arti?cial intel- gence, computational linguistics, computer science, philosophy, psychology - and in its ?nal phases - cognitive neuroscience and psycholinguistics. The funding covered a period of 12 years, which was split into four phases of 3 years each, ending in December of 2007. Every sub-period culminated in an intensive reviewing process, comprising written reports as well as on-site p- sentations and demonstrations to the external reviewers. We are most grateful to these reviewers for their extensive support and critical feedback; they contributed 1 their time and labor freely to the DFG, the independent and self-organized ins- tution of German scientists. The ?nal evaluation of the DFG reviewers judged the overall performance and the actual work with the highest possible mark, i.e. "excellent".

Innovative examination of augmentation technologies in terms of technical, social, and ethical considerations Usable as a supplemental text for a variety of courses, and also of interest to researchers and professionals in fields including: technical communication, digital communication, UX design, information technology, informatics, human factors, artificial intelligence, ethics, philosophy of technology, and sociology of technology First major work to combine technological, ethical, social, and rhetorical perspectives on human augmentation Additional cases and research material available at the authors' Fabric of Digital Life research database at https://fabricofdigitallife.com/

This book is a convergence of heterogeneous insights (from languages and literature, history, music, media and communications, computer science and information studies) which previously went their separate ways; now unified under a single framework for the purpose of preserving a unique heritage, the language. In a growing society like ours, description and documentation of human and scientific evidence/resources are improving. However, these resources have enjoyed cost-effective solutions for Western languages but are yet to flourish for African tone languages. By situating discussions around a universe of discourse, sufficient to engender cross-border interactions within the African context, this book shall break a dichotomy of challenges on adaptive processes required to unify resources to assist the development of modern solutions for the African domain.

More than ever, professional English is now cruising towards an enormous challenge in the European university context due to the extremely significant moment we are living in the European Higher Education Area (EHEA). The European convergence process is demanding immediate reflections, serious analyses, and profound reforms in specialized language teaching that lead to reach Bologna standards by 2010. This book aims to present an overview of professional English in the current academic landscape in Europe. It intends to shed light on a range of issues, both theoretical and practical, related to ESP, focusing on discourse analysis, corpus analysis, information and communication technologies, methodological approaches, curriculum design, and empirical research into language learning in broad terms. Because teachers need to be researchers and inquirers, this overview thus makes a contribution to the professional English field with the purpose of highlighting several important questions in the entire ESP academic mainstream. Scholars from different European universities explore specialized languages and document ESP teaching methodologies at university levels from a multidimensional perspective.

Computers are essential for the functioning of our society. Despite the incredible power of existing computers, computing technology is progressing beyond today's conventional models. Quantum Computing (QC) is surfacing as a promising disruptive technology. QC is built on the principles of quantum mechanics. QC can run algorithms that are not trivial to run on digital computers. QC systems are being developed for the discovery of new materials and drugs and improved methods for encoding information for secure communication over the Internet. Unprecedented new uses for this technology are bound to emerge from ongoing research. The development of conventional digital computing technology for the arts and humanities has been progressing in tandem with the evolution of computers since the 1950s. Today, computers are absolutely essential for the arts and humanities. Therefore, future developments in QC are most likely to impact on the way in which artists will create and perform, and how research in the humanities will be conducted. This book presents a comprehensive collection of chapters by pioneers of emerging interdisciplinary research at the crossroads of quantum computing, and the arts and humanities, from philosophy and social sciences to visual arts and music. Prof. Eduardo Reck Miranda is a composer and a professor in Computer Music at Plymouth University, UK, where he is a director of the Interdisciplinary Centre for Computer Music Research (ICCMR). His previous publications include the Springer titles Handbook of Artificial Intelligence for Music, Guide to Unconventional Computing for Music, Guide to Brain-Computer Music Interfacing and Guide to Computing for Expressive Music Performance.

Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP) to adapt existing phonemic MSA acoustic models with a small amount of dialectal ECA speech data. Speech recognition results indicate a significant increase in recognition accuracy compared to a baseline model trained with only ECA data.