Social media platforms have been ubiquitously used in our daily lives and are steadily transforming the ways people communicate, socialize and conduct business. However, the growing popularity of social media adversely leads to wild spread of unreliable information. This in turn inevitably creates serious pollution problem of the global social media environment, which is harmful against humanity. For example, President Donald Trump used social media strategically to win in the 2016 USA Presidential Election. But it was found that many messages he delivered over social media were unproven, if not untrue. This problem must be prevented at all cost and as soon as possible. Thus, analysis of social media content is a pressing issue. It is a timely and important research subject worldwide. However, the short and informal nature of social media messages renders conventional content analysis, which is based on natural language processing (NLP), ineffective. This volume consists of a collection of highly relevant scientific articles published by the authors in different international conferences and journals, and is divided into three distinct parts: (I) search and filtering; (II) opinion and sentiment analysis; and (III) event detection and summarization. This book presents the latest advances in NLP technologies for social media content analysis, especially content on microblogging platforms such as Twitter and Weibo.

This text presents the formal concepts underlying Computer Science.It starts with a wide introduction to Logic with an emphasis on reasoning and proof, with chapters on Program Verification and Prolog.The treatment of computability with Automata and Formal Languages stands out in several ways:The style is appropriate for both undergraduate and graduate classes.

This text presents the formal concepts underlying Computer Science.It starts with a wide introduction to Logic with an emphasis on reasoning and proof, with chapters on Program Verification and Prolog.The treatment of computability with Automata and Formal Languages stands out in several ways:The style is appropriate for both undergraduate and graduate classes.

As natural language processing spans many different disciplines, it is sometimes difficult to understand the contributions and the challenges that each of them presents. This book explores the special relationship between natural language processing and cognitive science, and the contribution of computer science to these two fields. It is based on the recent research papers submitted at the international workshops of Natural Language and Cognitive Science (NLPCS) which was launched in 2004 in an effort to bring together natural language researchers, computer scientists, and cognitive and linguistic scientists to collaborate together and advance research in natural language processing. The chapters cover areas related to language understanding, language generation, word association, word sense disambiguation, word predictability, text production and authorship attribution. This book will be relevant to students and researchers interested in the interdisciplinary nature of language processing.

The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions.

This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing.

Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing.

"Slav s work"Coarse-to-Fine Natural Language Processing "represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach."

Eugene Charniak (Brown University)"

This book records a unique attempt over a ten-year period to use stochastic optimization in the natural language processing domain. Setting the work against the background of the logical rule-based approach, the author provides a context for understanding the differences in assumptions about the nature of language and cognition.

The content of this textbook is organized as a theory of language for the construction of talking robots. The main topic is the mechanism of natural language communication in both the speaker and the hearer. In the third edition the author has modernized the text, leaving the overview of traditional, theoretical, and computational linguistics, analytic philosophy of language, and mathematical complexity theory with their historical backgrounds intact. The format of the empirical analyses of English and German syntax and semantics has been adapted to current practice; and Chaps. 22-24 have been rewritten to focus more sharply on the construction of a talking robot.

Natural language understanding is central to the goals of artificial intelligence. Any truly intelligent machine must be capable of carrying on a conversation: dialogue, particularly clarification dialogue, is essential if we are to avoid disasters caused by the misunderstanding of the intelligent interactive systems of the future. This book is an interim report on the grand enterprise of devising a machine that can use natural language as fluently as a human. What has really been achieved since this goal was first formulated in Turing's famous test? What obstacles still need to be overcome?

Addresses a central problem in cognitive science, concerning the learning procedures through which humans acquire and represent natural language. Brings together world leading scholars from a range of disciplines, includingcomputational linguistics, psychology, behavioural science, and mathematical linguistics. Will appeal to researchers in computational and mathematical linguistics, psychology and behavioral science, AI and NLP. Represents a wide spectrum of perspectives

This book evaluates the impact of relevant factors affecting the results of speech quality assessment studies carried out in crowdsourcing. The author describes how these factors relate to the test structure, the effect of environmental background noise, and the influence of language differences. He details multiple user-centered studies that have been conducted to derive guidelines for reliable collection of speech quality scores in crowdsourcing. Specifically, different questions are addressed such as the optimal number of speech samples to include in a listening task, the influence of the environmental background noise in the speech quality ratings, as well as methods for classifying background noise from web audio recordings, or the impact of language proficiency in the user perception of speech quality. Ultimately, the results of these studies contributed to the definition of the ITU-T Recommendation P.808 that defines the guidelines to conduct speech quality studies in crowdsourcing.

The 1990s saw a paradigm change in the use of corpus-driven methods in NLP. In the field of multilingual NLP (such as machine translation and terminology mining) this implied the use of parallel corpora. However, parallel resources are relatively scarce: many more texts are produced daily by native speakers of any given language than translated. This situation resulted in a natural drive towards the use of comparable corpora, i.e. non-parallel texts in the same domain or genre. Nevertheless, this research direction has not produced a single authoritative source suitable for researchers and students coming to the field. The proposed volume provides a reference source, identifying the state of the art in the field as well as future trends. The book is intended for specialists and students in natural language processing, machine translation and computer-assisted translation.

In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

Natural language understanding is central to the goals of artificial intelligence. Any truly intelligent machine must be capable of carrying on a conversation: dialogue, particularly clarification dialogue, is essential if we are to avoid disasters caused by the misunderstanding of the intelligent interactive systems of the future. This book is an interim report on the grand enterprise of devising a machine that can use natural language as fluently as a human. What has really been achieved since this goal was first formulated in Turing's famous test? What obstacles still need to be overcome?

This text introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, the and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms.

This Pivot reconsiders the controversial literary figure of Lin Shu and the debate surrounding his place in the history of Modern Chinese Literature. Although recent Chinese mainland research has recognized some of the innovations introduced by Lin Shu, he has often been labeled a 'rightist reformer' in contrast to 'leftist reformers' such as Chen Duxiu and the new wave scholars of the May Fourth Movement. This book provides a well-documented account of his place in the different polemics between these two circles ('conservatives' and 'reformers') and provides a more nuanced account of the different literary movements of the time. Notably, it argues that these differences were neither in content nor in politics, but in the methodological approach of both parties. Examining Lin Shu and the 'conservatives' advocated coexistence of both traditional and modern thought, the book provides background to the major changes occurring in the intellectual landscape of Modern China.

Recognizing that the generation of natural language is a goal- driven process, where many of the goals are pragmatic (i.e., interpersonal and situational) in nature, this book provides an overview of the role of pragmatics in language generation.
Each chapter states a problem that arises in generation, develops a pragmatics-based solution, and then describes how the solution is implemented in PAULINE, a language generator that can produce numerous versions of a single underlying message, depending on its setting.

In the not so distant future, we can expect a world where humans and robots coexist and interact with each other. For this to occur, we need to understand human traits, such as seeing, hearing, thinking, speaking, etc., and institute these traits in robots. The most essential feature necessary for robots to achieve is that of integrative multimedia understanding (IMU) which occurs naturally in humans. It allows us to assimilate pieces of information expressed through different modes such as speech, pictures, gestures, etc. The book describes how robots acquire traits like natural language understanding (NLU) as the central part of IMU. Mental image directed semantic theory (MIDST) is its core, and is based on the hypothesis that NLU is essentially the processing of mental image associated with natural language expressions, namely, mental-image based understanding (MBU). MIDST is intended to model omnisensory mental image in human and to afford a knowledge representation system in order for integrative management of knowledge subjective to cognitive mechanisms of intelligent entities such as humans and robots based on a mental image model visualized as 'Loci in Attribute Spaces' and its description language Lmd (mental image description language) to be employed for predicate logic with a systematic scheme for symbol-grounding. This language works as an interlingua among various kinds of information media, and has been applied to several versions of the intelligent system interlingual understanding model aiming at general system (IMAGES). Its latest version, i.e. conversation management system (CMS) simulates MBU and comprehends the user's intention through dialogue to find and solve problems, and finally, provides a response in text or animation. The book is aimed at researchers and students interested in artificial intelligence, robotics, and cognitive science. Based on philosophical considerations, the methodology will also have an appeal in linguistics, psychology, ontology, geography, and cartography. Key Features: Describes the methodology to provide robots with human-like capability of natural language understanding (NLU) as the central part of IMU Uses methodology that also relates to linguistics, psychology, ontology, geography, and cartography Examines current trends in machine translation

This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.

This book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language Processing (NLP). NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems. These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential. Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject. Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.

A reconsideration of the semantics of a lexical category prepositions that has recently witnessed a plethora of investigations. The volume approaches the issue first from a more general perspective, namely the extent to which insights into the meaning of prepositions give clues to the semantic struc

Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing. Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided. An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.

A comprehensive reference on the exciting growth area of spoken dialogs with computers, this text describes the components of a computer-based spoken dialog system, and will prove invaluable to researchers in industry and academia working on speech communication systems and for applications developers. This state-of-the-art book reviews the complete chain from microphone to speech synthesis. It provides methods, models, and algorithms for building a working system. Renato De Mori is coauthor of each chapter ensuring coherence and homogeneity throughout the text.
Spoken Dialogs with Computers covers in detail: transducers and microphone arrays, speech analysis and transformation, acoustic modeling and model training, language modeling, and knowledge integration for automatic speech recognition (ASR). The book also presents generation of word hypotheses, speaker adaptation, robustness and telephone application, use of syntactic and semantic knowledge, speech interpretation and dialog strategies, speech generation, and software system architectures for practical implementation.
Key Features
* All the necessary methods and models are provided for building a working systems and there is clear algorithmic presentation of the important components
* A section on automatic interpretation allows the building of a database query system in spoken language
* The book will be invaluable to researchers in industry and academia working on speech communication systems and for application developers in industry

Investigates the new world of computer conferencing and details how writers use language when their social interaction is exclusively enacted through text on screens.

This book examines interactive electronic discourse, exposing use of language that has the immediacy characteristic of speech and the permanence characteristic of writing. The authors created an asynchronous mainframe conference for language and linguistics classes in which they presented students with the task of analyzing the language used in original newspaper reports of the 1960s Civil Rights Sitlns. The authors observed how students wrote to each other across a wide range of social and virtual settings, how they built a real, if short-lived, community within and across campus boundaries, and how they handled conflict while avoiding confrontation on sensitive issues of race and power. The result is a study that details how people use language when their social interaction is exclusively enacted through text on screens, and how their exchange is affected by computer conferencing.

The students who wrote in the electronic conferences faced two interrelated tasks: participating in a multiparty "conversation" and negotiating the individual identities they presented to one another in their virtual space. Individual writers used their own idiolects to influence the form and content of electronic discourse, adapting their own tacit knowledge of conversational strategies and written discourse to the new medium, as they created a real, although temporary, community.

The techniques of natural language processing (NLP) have been widely applied in machine translation and automated message understanding, but have only recently been utilized in second language teaching. This book offers both an argument for and a critical examination of this new application, with an examination of how systems may be designed to exploit the power of NLP, accomodate its limitations, and minimize its risks. This volume marks the first collection of work in the U.S. and Canada that incorporates advanced human language technologies into language tutoring systems, covering languages as diverse as Arabic, Spanish, Japanese, and English.
The book is organized into sections that express the levels of analysis dealt with in learning and teaching a language and with the tasks of the student as writer, reader, conversant, and actor in the world. These sections bring together research by specialists in linguistics, artificial intelligence, psychology, instructional design, and language teaching. In addition to providing detailed descriptions of working systems, amply illustrated with screens from lesson and authoring interfaces, the contributors address a spectrum of common issues:
* What can current NLP technology contribute to computer-assisted language instruction and to research on language learning?
* How can this technology meet the demands of pedagogical theory for communicative language teaching in authentic contexts?
* How can designers constrain tutoring environments to ensure accurate analysis of learners' language?
* What can NLP-based systems teach us about language acquisition, about linguistic theory, and about theories of language pedagogy?
* What lessons have been learned in using these systems to date?
Discipline-specific issues are illuminated as well: the relative merits of the major syntactic frameworks for NLP-based language tutoring; the adaptation of theories like lexical conceptual structure to support semantic interpretation; the integration of input language with visual microworlds and dialogue games; the pragmatics of the tutoring discourse; the selection of instructional principles to guide system design; and the accomodation of design to individual differences and learner styles. A concluding section assesses this work from larger theoretical and practical perspectives -- experimental psychology and psycholinguistics, linguistics, language teaching, and second language acquisition research.