Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions.

This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms.

The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described.

This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

Dictation systems, read-aloud software for the blind, speech control of machinery, geographical information systems with speech input and output, and educational software with talking head' artificial tutorial agents are already on the market. The field is expanding rapidly, and new methods and applications emerge almost daily. But good sources of systematic information have not kept pace with the body of information needed for development and evaluation of these systems. Much of this information is widely scattered through speech and acoustic engineering, linguistics, phonetics, and experimental psychology.The Handbook of Multimodal and Spoken Dialogue Systems presents current and developing best practice in resource creation for speech input/output software and hardware. This volume brings experts in these fields together to give detailed how to' information and recommendations on planning spoken dialogue systems, designing and evaluating audiovisual and multimodal systems, and evaluating consumer off-the-shelf products.In addition to standard terminology in the field, the following topics are covered in depth: How to collect high quality data for designing, training, and evaluating multimodal and speech dialogue systems; How to evaluate real-life computer systems with speech input and output; How to describe and model human-computer dialogue precisely and in depth.Also included: A fully searchable CD-ROM containing a hypertext version of the book in HTML format for fast look-up of specific points, convenient desktop use, and lightweight mobile reference; and The first systematic medium-scale compendium of terminology with definitions.This handbook has been especially designed for theneeds of development engineers, decision-makers, researchers, and advanced level students in the fields of speech technology, multimodal interfaces, multimedia, computational linguistics, and phonetics.

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 book presents a collection of papers on the issue of focus in its broadest sense. While commonly being considered as related to phenomena such as presupposition and anaphora, focusing is much more widely spread, and it is this pervasiveness that this collection addresses. The volume explicitly aims to bring together theoretical, psychological, and descriptive approaches to focus, at the same time maintaining the overall interest in how these notions apply to the larger problem of evolving some formal representation of the semantic aspects of linguistic content. The contributed papers to this volume have been reworked from a selection of original work presented at a conference held in 1994 in Schloss Wolfsbrunnen in Germany.

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

This book proposes a new model for the translation-oriented analysis of multimodal source texts. The author guides the reader through semiotics, multimodality, pragmatics and translation studies on a quest for the meaning-making mechanics of texts that combine images and words. She openly challenges the traditional view that sees translators focusing their attention mostly on the linguistic aspect of source material in their work. The central theoretical pivot around which the analytical model revolves is that multimodal texts communicate through individual images and linguistic units, as well as through the interaction among textual resources and the text's interaction with its context of reference. This three-dimensional view offers a holistic understanding of multimodal texts and their potential translation issues to help translators improve the way they communicate multimodally across languages and cultures. This book will appeal to researchers in the fields of translation studies, multimodality and pragmatics.

A practical introduction to essential topics at the core of computer science

Automata, formal language, and complexity theory are central to the understanding of computer science. This book provides, in an accessible, practically oriented style, a thorough grounding in these topics for practitioners and students on all levels.

Based on the authors’ belief that the problem-solving approach is the most effective, Problem Solving in Automata, Languages, and Complexity collects a rich variety of worked examples, questions, and exercises designed to ensure understanding and mastery of the subject matter. Building from the fundamentals for beginning engineers to more advanced concepts, the book examines the most common topics in the field, including:

• Finite-state automata
• Context-free grammars
• Turing machines
• Recursive and recursively enumerable languages
• Computability theory
• Complexity classes
• NP-completeness

Focused, practical, and versatile, Problem Solving in Automata, Languages, and Complexity gives students and engineers a solid grounding in essential areas in computer science.

Recent developments in artificial intelligence, especially neural network and deep learning technology, have led to rapidly improving performance in voice assistants such as Siri and Alexa. Over the next few years, capability will continue to improve and become increasingly personalised. Today's voice assistants will evolve into virtual personal assistants firmly embedded within our everyday lives. Told through the view of a fictitious personal assistant called Cyba, this book provides an accessible but detailed overview of how a conversational voice assistant works, especially how it understands spoken language, manages conversations, answers questions and generates responses. Cyba explains through examples and diagrams the neural network technology underlying speech recognition and synthesis, natural language understanding, knowledge representation, conversation management, language translation and chatbot technology. Cyba also explores the implications of this rapidly evolving technology for security, privacy and bias, and gives a glimpse of future developments. Cyba's website can be found at HeyCyba.com.

Parsing with Principles and Classes of Information presents a parser based on current principle-based linguistic theories for English. It argues that differences in the kind of information being computed, whether lexical, structural or syntactic, play a crucial role in the mapping from grammatical theory to parsing algorithms. The direct encoding of homogeneous classes of information has computational and cognitive advantages, which are discussed in detail. Phrase structure is built by using a fast algorithm and compact reference tables. A quantified comparison of different compilation methods shows that lexical and structural information are most compactly represented by separate tables. This finding is reconciled to evidence on the resolution of lexical ambiguity, as an approach to the modularization of information. The same design is applied to the efficient computation of long- distance dependencies. Incremental parsing using bottom-up tabular algorithms is discussed in detail. Finally, locality restrictions are calculated by a parametric algorithm. Students of linguistics, parsing and psycholinguistics will find this book a useful resource on issues related to the implementation of current linguistic theories, using computational and cognitive plausible algorithms.

This book takes concepts developed by researchers in theoretical computer science and adapts and applies them to the study of natural language meaning. Summarizing more than a decade of research, Chris Barker and Chung-chieh Shan put forward the Continuation Hypothesis: that the meaning of a natural language expression can depend on its own continuation. In Part I, the authors develop a continuation-based theory of scope and quantificational binding and provide an explanation for order sensitivity in scope-related phenomena such as scope ambiguity, crossover, superiority, reconstruction, negative polarity licensing, dynamic anaphora, and donkey anaphora. Part II outlines an innovative substructural logic for reasoning about continuations and proposes an analysis of the compositional semantics of adjectives such as 'same' in terms of parasitic and recursive scope. It also shows that certain cases of ellipsis should be treated as anaphora to a continuation, leading to a new explanation for a subtype of sluicing known as sprouting. The book makes a significant contribution to work on scope, reference, quantification, and other central aspects of semantics and will appeal to semanticists in linguistics and philosophy at graduate level and above.

Peer reviewed articles from the Natural Language Processing and Cognitive Science (NLPCS) 2014 meeting in October 2014 workshop. The meeting fosters interactions among researchers and practitioners in NLP by taking a Cognitive Science perspective. Articles cover topics such as artificial intelligence, computational linguistics, psycholinguistics, cognitive psychology and language learning.

This book serves as a starting point for Semantic Web (SW) students and researchers interested in discovering what Natural Language Processing (NLP) has to offer. NLP can effectively help uncover the large portions of data held as unstructured text in natural language, thus augmenting the real content of the Semantic Web in a significant and lasting way. The book covers the basics of NLP, with a focus on Natural Language Understanding (NLU), referring to semantic processing, information extraction and knowledge acquisition, which are seen as the key links between the SW and NLP communities. Major emphasis is placed on mining sentences in search of entities and relations. In the course of this "quest", challenges will be encountered for various text analysis tasks, including part-of-speech tagging, parsing, semantic disambiguation, named entity recognition and relation extraction. Standard algorithms associated with these tasks are presented to provide an understanding of the fundamental concepts. Furthermore, the importance of experimental design and result analysis is emphasized, and accordingly, most chapters include small experiments on corpus data with quantitative and qualitative analysis of the results. This book is divided into four parts. Part I "Searching for Entities in Text" is dedicated to the search for entities in textual data. Next, Part II "Working with Corpora" investigates corpora as valuable resources for NLP work. In turn, Part III "Semantic Grounding and Relatedness" focuses on the process of linking surface forms found in text to entities in resources. Finally, Part IV "Knowledge Acquisition" delves into the world of relations and relation extraction. The book also includes three appendices: "A Look into the Semantic Web" gives a brief overview of the Semantic Web and is intended to bring readers less familiar with the Semantic Web up to speed, so that they too can fully benefit from the material of this book. "NLP Tools and Platforms" provides information about NLP platforms and tools, while "Relation Lists" gathers lists of relations under different categories, showing how relations can be varied and serve different purposes. And finally, the book includes a glossary of over 200 terms commonly used in NLP. The book offers a valuable resource for graduate students specializing in SW technologies and professionals looking for new tools to improve the applicability of SW techniques in everyday life - or, in short, everyone looking to learn about NLP in order to expand his or her horizons. It provides a wealth of information for readers new to both fields, helping them understand the underlying principles and the challenges they may encounter.

Computational Psycholinguistics: An Interdisciplinary Approach to the Study of Language investigates the architecture and mechanisms which underlie the human capacity to process language. It is the first such study to integrate modern syntactic theory, cross-linguistic psychological evidence, and modern computational techniques in constructing a model of the human sentence processing mechanism. The monograph follows the rationalist tradition, arguing the central role of modularity and universal grammar in a theory of human linguistic performance. It refines the notion of `modularity of mind', and presents a distributed model of syntactic processing which consists of modules aligned with the various informational `types' associated with modern linguistic theories. By considering psycholinguistic evidence from a range of languages, a small number of processing principles are motivated and are demonstrated to hold universally. It is also argued that the behavior of modules, and the strategies operative within them, can be derived from an overarching `Principle of Incremental Comprehension'. Audience: The book is recommended to all linguists, psycholinguists, computational linguists, and others interested in a unified and interdisciplinary study of the human language faculty.

This book encompasses a collection of topics covering recent advances that are important to the Arabic language in areas of natural language processing, speech and image analysis. This book presents state-of-the-art reviews and fundamentals as well as applications and recent innovations.The book chapters by top researchers present basic concepts and challenges for the Arabic language in linguistic processing, handwritten recognition, document analysis, text classification and speech processing. In addition, it reports on selected applications in sentiment analysis, annotation, text summarization, speech and font analysis, word recognition and spotting and question answering.Moreover, it highlights and introduces some novel applications in vital areas for the Arabic language. The book is therefore a useful resource for young researchers who are interested in the Arabic language and are still developing their fundamentals and skills in this area. It is also interesting for scientists who wish to keep track of the most recent research directions and advances in this area.

Recent Advances in Example-Based Machine Translation is of relevance to researchers and program developers in the field of Machine Translation and especially Example-Based Machine Translation, bilingual text processing and cross-linguistic information retrieval. It is also of interest to translation technologists and localisation professionals.

Recent Advances in Example-Based Machine Translation fills a void, because it is the first book to tackle the issue of EBMT in depth. It gives a state-of-the-art overview of EBMT techniques and provides a coherent structure in which all aspects of EBMT are embedded. Its contributions are written by long-standing researchers in the field of MT in general, and EBMT in particular. This book can be used in graduate-level courses in machine translation and statistical NLP.

Two Top Industry Leaders Speak Out Judith Markowitz When Amy asked me to co-author the foreword to her new book on advances in speech recognition, I was honored. Amy's work has always been infused with c- ative intensity, so I knew the book would be as interesting for established speech professionals as for readers new to the speech-processing industry. The fact that I would be writing the foreward with Bill Scholz made the job even more enjoyable. Bill and I have known each other since he was at UNISYS directing projects that had a profound impact on speech-recognition tools and applications. Bill Scholz The opportunity to prepare this foreword with Judith provides me with a rare oppor- nity to collaborate with a seasoned speech professional to identify numerous signi- cant contributions to the field offered by the contributors whom Amy has recruited. Judith and I have had our eyes opened by the ideas and analyses offered by this collection of authors. Speech recognition no longer needs be relegated to the ca- gory of an experimental future technology; it is here today with sufficient capability to address the most challenging of tasks. And the point-click-type approach to GUI control is no longer sufficient, especially in the context of limitations of mode- day hand held devices. Instead, VUI and GUI are being integrated into unified multimodal solutions that are maturing into the fundamental paradigm for comput- human interaction in the future.

The key assumption in this text is that machine translation is not merely a mechanical process but in fact requires a high level of linguistic sophistication, as the nuances of syntax, semantics and intonation cannot always be conveyed by modern technology. The increasing dependence on artificial communication by private and corporate users makes this research area an invaluable element when teaching linguistic theory.

In the global research community, English has become the main language of scholarly publishing in many disciplines. At the same time, online machine translation systems have become increasingly easy to access and use. Is this a researcher's match made in heaven, or the road to publication perdition? Here Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. For scholars, they explain how machine translation works, how it is (or could be) used for scholarly communication, and how both native and non-native English-speakers can write in a translation-friendly way in order to harness its potential. Native English speakers can continue to write in English, but expand the global reach of their research by making it easier for their peers around the world to access and understand their works, while non-native English speakers can write in their mother tongues, but leverage machine translation technology to help them produce draft publications in English. For academic librarians, the authors provide a framework for supporting researchers in all disciplines as they grapple with producing translation-friendly texts and using machine translation for scholarly communication-a form of support that will only become more important as campuses become increasingly international and as universities continue to strive to excel on the global stage. Machine Translation and Global Research is a must-read for scientists, researchers, students, and librarians eager to maximize the global reach and impact of any form of scholarly work.

The Generalized LR parsing algorithm (some call it "Tomita's algorithm") was originally developed in 1985 as a part of my Ph.D thesis at Carnegie Mellon University. When I was a graduate student at CMU, I tried to build a couple of natural language systems based on existing parsing methods. Their parsing speed, however, always bothered me. I sometimes wondered whether it was ever possible to build a natural language parser that could parse reasonably long sentences in a reasonable time without help from large mainframe machines. At the same time, I was always amazed by the speed of programming language compilers, because they can parse very long sentences (i.e., programs) very quickly even on workstations. There are two reasons. First, programming languages are considerably simpler than natural languages. And secondly, they have very efficient parsing methods, most notably LR. The LR parsing algorithm first precompiles a grammar into an LR parsing table, and at the actual parsing time, it performs shift-reduce parsing guided deterministically by the parsing table. So, the key to the LR efficiency is the grammar precompilation; something that had never been tried for natural languages in 1985. Of course, there was a good reason why LR had never been applied for natural languages; it was simply impossible. If your context-free grammar is sufficiently more complex than programming languages, its LR parsing table will have multiple actions, and deterministic parsing will be no longer possible.

Parsing Efficiency is crucial when building practical natural language systems. 'Ibis is especially the case for interactive systems such as natural language database access, interfaces to expert systems and interactive machine translation. Despite its importance, parsing efficiency has received little attention in the area of natural language processing. In the areas of compiler design and theoretical computer science, on the other hand, parsing algorithms 3 have been evaluated primarily in terms of the theoretical worst case analysis (e.g. lXn", and very few practical comparisons have been made. This book introduces a context-free parsing algorithm that parses natural language more efficiently than any other existing parsing algorithms in practice. Its feasibility for use in practical systems is being proven in its application to Japanese language interface at Carnegie Group Inc., and to the continuous speech recognition project at Carnegie-Mellon University. This work was done while I was pursuing a Ph.D degree at Carnegie-Mellon University. My advisers, Herb Simon and Jaime Carbonell, deserve many thanks for their unfailing support, advice and encouragement during my graduate studies. I would like to thank Phil Hayes and Ralph Grishman for their helpful comments and criticism that in many ways improved the quality of this book. I wish also to thank Steven Brooks for insightful comments on theoretical aspects of the book (chapter 4, appendices A, B and C), and Rich Thomason for improving the linguistic part of tile book (the very beginning of section 1.1).

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.

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?