This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.

This book constitutes the proceedings of the 20th International Conference on Developments in Language Theory, DLT 2016, held in Montreal, QC, Canada, in July 2016. The 32 full papers and 4 abstracts of invited papers presented were carefully reviewed and selected from 48 submissions. This volume presents current developments in formal languages and automata, especially from the following topics and areas: combinatorial and algebraic properties of words and languages; grammars, acceptors and transducers for strings, trees, graphs, arrays; algebraic theories for automata and languages; codes; efficient text algorithms; symbolic dynamics; decision problems; relationships to complexity theory and logic; picture description and analysis; polyominoes and bidimentional patterns; cryptography; concurrency; cellular automata; bio-inspried computing; quantum computing.

SMIL 3.0: Multimedia for the Web, Mobile Devices and Daisy Talking Books is a revised introduction to - and resource guide for - the W3C SMIL language. It covers all aspects of the SMIL specification and covers all of SMIL's implem- tation profiles, from the desktop through the world of mobile SMIL devices. Based on the first version of the book, which covered SMIL 2.0, this edition has been updated with information from the past two releases of the SMIL l- guage. We have benefitted from comments and suggestions from many readers of the first edition, and have produced what we feel is the most comprehensive guide to SMIL available anywhere. Motivation for this Book While we were working on various phases of the SMIL recommendations, it became clear to us that the richness of the SMIL language could easily ov- whelm many Web authors and designers. In the 500+ pages that the SYMM working group needed to describe the 30+ SMIL elements and the 150+ SMIL attributes, there was not much room for background information or extensive examples. The focus of the specification was on implementation aspects of the SMIL language, not on the rationale or the potential uses of SMIL's declarative power.

This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative, as they elegantly solve many of the shortcomings of phrase-based models. The heart of this book is a detailed introduction to decoding for syntax-based models. The book begins with an overview of synchronous-context free grammar (SCFG) and synchronous tree-substitution grammar (STSG) along with their associated statistical models. It also describes how three popular instantiations (Hiero, SAMT, and GHKM) are learned from parallel corpora. It introduces and details hypergraphs and associated general algorithms, as well as algorithms for decoding with both tree and string input. Special attention is given to efficiency, including search approximations such as beam search and cube pruning, data structures, and parsing algorithms. The book consistently highlights the strengths (and limitations) of syntax-based approaches, including their ability to generalize phrase-based translation units, their modeling of specific linguistic phenomena, and their function of structuring the search space.

This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format. It places a special focus on the challenges and opportunities of domain-sensitive temporal tagging. After providing background knowledge on the concept of time, the book continues with a comprehensive survey of current research on temporal tagging. The authors provide an overview of existing techniques and tools, and highlight key issues that need to be addressed. This book is a valuable resource for researchers and application developers who need to become familiar with the topic and want to know the recent trends, current tools and techniques, as well as different application domains in which temporal information is of utmost importance. Due to the prevalence of temporal expressions in diverse types of documents and the importance of temporal information in any information space, temporal tagging is an important task in natural language processing (NLP), and applications of several domains can benefit from the output of temporal taggers to provide more meaningful and useful results. In recent years, temporal tagging has been an active field in NLP and computational linguistics. Several approaches to temporal tagging have been proposed, annotation standards have been developed, gold standard data sets have been created, and research competitions have been organized. Furthermore, some temporal taggers have also been made publicly available so that temporal tagging output is not just exploited in research, but is finding its way into real world applications. In addition, this book particularly focuses on domain-specific temporal tagging of documents. This is a crucial aspect as different types of documents (e.g., news articles, narratives, and colloquial texts) result in diverse challenges for temporal taggers and should be processed in a domain-sensitive manner.

This book conveys the fundamentals of Linked Lexical Knowledge Bases (LLKB) and sheds light on their different aspects from various perspectives, focusing on their construction and use in natural language processing (NLP). It characterizes a wide range of both expert-based and collaboratively constructed lexical knowledge bases. Only basic familiarity with NLP is required and this book has been written for both students and researchers in NLP and related fields who are interested in knowledge-based approaches to language analysis and their applications. Lexical Knowledge Bases (LKBs) are indispensable in many areas of natural language processing, as they encode human knowledge of language in machine readable form, and as such, they are required as a reference when machines attempt to interpret natural language in accordance with human perception. In recent years, numerous research efforts have led to the insight that to make the best use of available knowledge, the orchestrated exploitation of different LKBs is necessary. This allows us to not only extend the range of covered words and senses, but also gives us the opportunity to obtain a richer knowledge representation when a particular meaning of a word is covered in more than one resource. Examples where such an orchestrated usage of LKBs proved beneficial include word sense disambiguation, semantic role labeling, semantic parsing, and text classification. This book presents different kinds of automatic, manual, and collaborative linkings between LKBs. A special chapter is devoted to the linking algorithms employing text-based, graph-based, and joint modeling methods. Following this, it presents a set of higher-level NLP tasks and algorithms, effectively utilizing the knowledge in LLKBs. Among them, you will find advanced methods, e.g., distant supervision, or continuous vector space models of knowledge bases (KB), that have become widely used at the time of this book's writing. Finally, multilingual applications of LLKB's, such as cross-lingual semantic relatedness and computer-aided translation are discussed, as well as tools and interfaces for exploring LLKBs, followed by conclusions and future research directions.

This book constitutes the refereed proceedings of the 7th International Conference of the CLEF Initiative, CLEF 2016, held in Toulouse, France, in September 2016. The 10 full papers and 8 short papers presented together with 5 best of the labs papers were carefully reviewed and selected from 36 submissions. In addition to these talks, this volume contains the results of 7 benchmarking labs reporting their year long activities in overview talks and lab sessions. The papers address all aspects of information access in any modality and language and cover a broad rangeof topics in the fields of multilingual and multimodal information access evaluation.

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.

This book constitutes the refereed proceedings of the 15th Industrial Conference on Advances in Data Mining, ICDM 2015, held in Hamburg, Germany, in July 2015. The 16 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry and society.

This book constitutes the refereed proceedings of the 6th Language and Technology Conference: Challenges for Computer Science and Linguistics, LTC 2013, held in Poznan, Poland, in December 2013. The 31 revised and in many cases substantially extended papers presented in this volume were carefully reviewed and selected from 103 submissions.The papers selected to this volume belong to various fields of Human Language Technologies and illustrate a large thematic coverage of the LTC conferences. To make the presentation of the papers possibly transparent we have "structured" them into 9 chapters. These are: Speech Processing, Morphology, Parsing Related Issues, Computational Semantics, Digital Language Resources, Ontologies and Wordnets, Written Text and Document Processing, Information and Data Extraction, and Less-Resourced Languages.

This book constitutes the refereed proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, held in Passau, Germany, in June 2015. The 18 full papers, 15 short papers, 14 poster and demonstration papers presented were carefully reviewed and selected from 100 submissions. The papers cover the following topics: information extraction, distributional semantics, querying and question answering systems, context-aware NLP, cognitive and semantic computing, sentiment and opinion analysis, information extraction and social media, NLP and usability, text classification and extraction, and posters and demonstrations.

This book constitutes the refereed proceedings of the 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 full papers and 27 short papers presented were carefully reviewed and selected from 97 submissions. The focus of the conference was on the following subjects: actions and behaviours, audio and visual recognition, natural language processing, reasoning and learning, streams and distributed computing.

This book constitutes the refereed proceedings of the 5th International Workshop on Controlled Natural Language, CNL 2016, held in Aberdeen, UK, in July 2016. The 11 full papers presented were carefully reviewed and selected from 15 submissions. The topics range from natural languages which are controlled, to controlled languages with a natural language flavour; and from more theoretical results to interfaces, reasoning engines and real-life applications of CNLs.

This book constitutes the refereed proceedings of the 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, held in Salford, UK, in June 2016. The 17 full papers, 22 short papers, and 13 poster papers presented were carefully reviewed and selected from 83 submissions. The papers cover the following topics: theoretical aspects, algorithms, applications, architectures for applied and integrated NLP, resources for applied NLP, and other aspects of NLP.

The two volumes LNCS 9041 and 9042 constitute the proceedings of the 16th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2015, held in Cairo, Egypt, in April 2015. The total of 95 full papers presented was carefully reviewed and selected from 329 submissions. They were organized in topical sections on grammar formalisms and lexical resources; morphology and chunking; syntax and parsing; anaphora resolution and word sense disambiguation; semantics and dialogue; machine translation and multilingualism; sentiment analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information retrieval, question answering, and information extraction; text classification; speech processing; and applications.

The two volumes LNCS 9041 and 9042 constitute the proceedings of the 16th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2015, held in Cairo, Egypt, in April 2015. The total of 95 full papers presented was carefully reviewed and selected from 329 submissions. They were organized in topical sections on grammar formalisms and lexical resources; morphology and chunking; syntax and parsing; anaphora resolution and word sense disambiguation; semantics and dialogue; machine translation and multilingualism; sentiment analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information retrieval, question answering, and information extraction; text classification; speech processing; and applications.

This book constitutes the thoroughly refereed proceedings of the 5th Joint International Semantic Technology Conference, JIST 2015, held in Yichang, China, in November 2015. The theme of the JIST 2015 conference was "Big Data and Social Media". The JIST 2015 conference consisted of main technical tracks including 2 keynotes, 2 invited talks, a regular technical paper track (full and short papers), an in-use track, a poster and demo session, workshop, and tutorial. The 14 full and 8 short papers in this volume were carefully reviewed and selected from 43 submissions. The paper cover the following topics: ontology and reasoning, linked data, learning and discovery, RDF and query, knowledge graph, knowledge integration, query and recommendation, and applications of semantic technologies.

The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren-including simile, analogy, and conceptual blending-that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.

This book provides a thorough introduction to the subfield of theoretical computer science known as grammatical inference from a computational linguistic perspective. Grammatical inference provides principled methods for developing computationally sound algorithms that learn structure from strings of symbols. The relationship to computational linguistics is natural because many research problems in computational linguistics are learning problems on words, phrases, and sentences: What algorithm can take as input some finite amount of data (for instance a corpus, annotated or otherwise) and output a system that behaves "correctly" on specific tasks? Throughout the text, the key concepts of grammatical inference are interleaved with illustrative examples drawn from problems in computational linguistics. Special attention is paid to the notion of "learning bias." In the context of computational linguistics, such bias can be thought to reflect common (ideally universal) properties of natural languages. This bias can be incorporated either by identifying a learnable class of languages which contains the language to be learned or by using particular strategies for optimizing parameter values. Examples are drawn largely from two linguistic domains (phonology and syntax) which span major regions of the Chomsky Hierarchy (from regular to context-sensitive classes). The conclusion summarizes the major lessons and open questions that grammatical inference brings to computational linguistics. Table of Contents: List of Figures / List of Tables / Preface / Studying Learning / Formal Learning / Learning Regular Languages / Learning Non-Regular Languages / Lessons Learned and Open Problems / Bibliography / Author Biographies

This book constitutes the refereed proceedings of the 18th International Conference on Text, Speech and Dialogue, TSD 2015, held in Pilsen, Czech Republic, in September 2015. The 67 papers presented together with 3 invited papers were carefully reviewed and selected from 138 submissions. They focus on topics such as corpora and language resources; speech recognition; tagging, classification and parsing of text and speech; speech and spoken language generation; semantic processing of text and speech; integrating applications of text and speech processing; automatic dialogue systems; as well as multimodal techniques and modelling.

There are not many people who can be said to have influenced and impressed researchers in so many disparate areas and language-geographic fields as Lauri Carlson, as is evidenced in the present Festschrift. His insight and acute linguistic sensitivity and linguistic rationality have spawned findings and research work in many areas, from non-standard etymology to hardcore formal linguistics, not forgetting computational areas such as parsing, terminological databases, and, last but not least, machine translation. In addition to his renowned and widely acknowledged insights in tense and aspect and its relationship with nominal quantification, and his ground-breaking work in dialog using game-theoretic machinery, Lauri has in the last fifteen years as Professor of Language Theory and Translation Technology contributed immensely to areas such as translation, terminology and general applications of computational linguistics. The three editors of the present volume have successfully performed doctoral studies under Lauri's supervision, and wish with this volume to pay tribute to his supervision and to his influence in matters associated with research and scientific, linguistic and philosophical inquiry, as well as to his humanity and friendship.

The attempt to spot deception through its correlates in human behavior has a long history. Until recently, these efforts have concentrated on identifying individual "cues" that might occur with deception. However, with the advent of computational means to analyze language and other human behavior, we now have the ability to determine whether there are consistent clusters of differences in behavior that might be associated with a false statement as opposed to a true one. While its focus is on verbal behavior, this book describes a range of behaviors-physiological, gestural as well as verbal-that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and "real-world" data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, and fraud; and false product reviews. Having established the background, this book concentrates on computational analyses of deceptive verbal behavior that have enabled the field of deception studies to move from individual cues to overall differences in behavior. The computational work is organized around the features used for classification from -gram through syntax to predicate-argument and rhetorical structure. The book concludes with a set of open questions that the computational work has generated.

This book constitutes the refereed proceedings of the International Conference on Intelligent Computer Mathematics, CICM 2015, held in Washington, DC, USA, in July 2015. The 16 full papers and 9 short papers presented together with two invited talks plus one abstract were carefully reviewed and selected from a total of 43 submissions. The papers are organized in topical sections following the tracks of the conference: Invited Talks; Calculemus; Digital Mathematics Libraries; Mathematical Knowledge Management; Projects and Surveys; Systems and Data.

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

Collaboratively Constructed Language Resources (CCLRs) such as Wikipedia, Wiktionary, Linked Open Data, and various resources developed using crowdsourcing techniques such as Games with a Purpose and Mechanical Turk have substantially contributed to the research in natural language processing (NLP). Various NLP tasks utilize such resources to substitute for or supplement conventional lexical semantic resources and linguistically annotated corpora. These resources also provide an extensive body of texts from which valuable knowledge is mined. There are an increasing number of community efforts to link and maintain multiple linguistic resources. This book aims offers comprehensive coverage of CCLR-related topics, including their construction, utilization in NLP tasks, and interlinkage and management. Various Bachelor/Master/Ph.D. programs in natural language processing, computational linguistics, and knowledge discovery can use this book both as the main text and as a supplementary reading. The book also provides a valuable reference guide for researchers and professionals for the above topics.