This book provides a comprehensive introduction to the conversational interface, which is becoming the main mode of interaction with virtual personal assistants, smart devices, various types of wearable, and social robots. The book consists of four parts. Part I presents the background to conversational interfaces, examining past and present work on spoken language interaction with computers. Part II covers the various technologies that are required to build a conversational interface along with practical chapters and exercises using open source tools. Part III looks at interactions with smart devices, wearables, and robots, and discusses the role of emotion and personality in the conversational interface. Part IV examines methods for evaluating conversational interfaces and discusses future directions.

This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to today's state-of-the-art approaches, which use advanced empirically grounded techniques, automatic knowledge acquisition, and refined linguistic modeling to make a real difference in real-world applications. Anaphora and coreference resolution both refer to the process of linking textual phrases (and, consequently, the information attached to them) within as well as across sentence boundaries, and to the same discourse referent. The book offers an overview of recent research advances, focusing on practical, operational approaches and their applications. In part I (Background), it provides a general introduction, which succinctly summarizes the linguistic, cognitive, and computational foundations of anaphora processing and the key classical rule- and machine-learning-based anaphora resolution algorithms. Acknowledging the central importance of shared resources, part II (Resources) covers annotated corpora, formal evaluation, preprocessing technology, and off-the-shelf anaphora resolution systems. Part III (Algorithms) provides a thorough description of state-of-the-art anaphora resolution algorithms, covering enhanced machine learning methods as well as techniques for accomplishing important subtasks such as mention detection and acquisition of relevant knowledge. Part IV (Applications) deals with a selection of important anaphora and coreference resolution applications, discussing particular scenarios in diverse domains and distilling a best-practice model for systematically approaching new application cases. In the concluding part V (Outlook), based on a survey conducted among the contributing authors, the prospects of the research field of anaphora processing are discussed, and promising new areas of interdisciplinary cooperation and emerging application scenarios are identified. Given the book's design, it can be used both as an accompanying text for advanced lectures in computational linguistics, natural language engineering, and computer science, and as a reference work for research and independent study. It addresses an audience that includes academic researchers, university lecturers, postgraduate students, advanced undergraduate students, industrial researchers, and software engineers.

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

Universal codes efficiently compress sequences generated by stationary and ergodic sources with unknown statistics, and they were originally designed for lossless data compression. In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area. The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series; the second chapter describes applications of selected statistical methods to cryptography, including attacks on block ciphers; and the third chapter describes a homogeneity test used to determine authorship of literary texts. The book will be useful for researchers and advanced students in information theory, mathematical statistics, time-series analysis, and cryptography. It is assumed that the reader has some grounding in statistics and in information theory.

This book constitutes the refereed proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, held in Paris, France, in June 2018. The 18 full papers, 26 short papers, and 9 poster papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking.

This book collects research contributions concerning quantitative approaches to characterize originality and universality in language. The target audience comprises researchers and experts in the field but the book may also be beneficial for graduate students. Creativity might be considered as a morphogenetic process combining universal features with originality. While quantitative methods applied to text and music reveal universal features of language and music, originality is a highly appreciated feature of authors, composers, and performers. In this framework, the different methods of traditional problems of authorship attribution and document classification provide important insights on how to quantify the unique features of authors, composers, and styles. Such unique features contrast, and are restricted by, universal signatures, such as scaling laws in word-frequency distribution, entropy measures, long-range correlations, among others. This interplay between innovation and universality is also an essential ingredient of methods for automatic text generation. Innovation in language becomes relevant when it is imitated and spread to other speakers and musicians. Modern digital databases provide new opportunities to characterize and model the creation and evolution of linguistic innovations on historical time scales, a particularly important example of the more general problem of spreading of innovations in complex social systems. This multidisciplinary book combines scientists from various different backgrounds interested in quantitative analysis of variations (synchronic and diachronic) in language and music. The aim is to obtain a deeper understanding of how originality emerges, can be quantified, and propagates.

This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

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.

This book constitutes the proceedings of the 5th InternationalConference on Algorithms for Computational Biology, AlCoB 2018, held in Hong Kong, China, in June 2018. The 11 full papers presented together with 1 invited paper were carefully reviewed and selected from 20 submissions. They are organized in the following topical sections: Phylogenetics, Sequence Rearrangement and Analysis, Systems Biology and Other Biological Processes.

This second edition provides a systematic introduction to the work and views of the emerging patent-search research and innovation communities as well as an overview of what has been achieved and, perhaps even more importantly, of what remains to be achieved. It revises many of the contributions of the first edition and adds a significant number of new ones. The first part "Introduction to Patent Searching" includes two overview chapters on the peculiarities of patent searching and on contemporary search technology respectively, and thus sets the scene for the subsequent parts. The second part on "Evaluating Patent Retrieval" then begins with two chapters dedicated to patent evaluation campaigns, followed by two chapters discussing complementary issues from the perspective of patent searchers and from the perspective of related domains, notably legal search. "High Recall Search" includes four completely new chapters dealing with the issue of finding only the relevant documents in a reasonable time span. The last (and with six papers the largest) part on "Special Topics in Patent Information Retrieval" covers a large spectrum of research in the patent field, from classification and image processing to translation. Lastly, the book is completed by an outlook on open issues and future research. Several of the chapters have been jointly written by intellectual property and information retrieval experts. However, members of both communities with a background different to that of the primary author have reviewed the chapters, making the book accessible to both the patent search community and to the information retrieval research community. It also not only offers the latest findings for academic researchers, but is also a valuable resource for IP professionals wanting to learn about current IR approaches in the patent domain.

This volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures. The book is of interest primarily to MT specialists, but also - in the wider fields of Computational Linguistics, Machine Learning and Data Mining - to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-ninth issue is a regular issue with 10 selected papers.

This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>

The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally. This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties. It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.

This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology.

This book presents recent advances in nonlinear speech processing beyond nonlinear techniques. It shows that it exploits heuristic and psychological models of human interaction in order to succeed in the implementations of socially believable VUIs and applications for human health and psychological support. The book takes into account the multifunctional role of speech and what is "outside of the box" (see Bjoern Schuller's foreword). To this aim, the book is organized in 6 sections, each collecting a small number of short chapters reporting advances "inside" and "outside" themes related to nonlinear speech research. The themes emphasize theoretical and practical issues for modelling socially believable speech interfaces, ranging from efforts to capture the nature of sound changes in linguistic contexts and the timing nature of speech; labors to identify and detect speech features that help in the diagnosis of psychological and neuronal disease, attempts to improve the effectiveness and performance of Voice User Interfaces, new front-end algorithms for the coding/decoding of effective and computationally efficient acoustic and linguistic speech representations, as well as investigations capturing the social nature of speech in signaling personality traits, emotions and improving human machine interactions.

The two-volume set LNCS 9623 + 9624 constitutes revised selected papers from the CICLing 2016 conference which took place in Konya, Turkey, in April 2016. The total of 89 papers presented in the two volumes was carefully reviewed and selected from 298 submissions. The book also contains 4 invited papers and a memorial paper on Adam Kilgarriff's Legacy to Computational Linguistics. The papers are organized in the following topical sections: Part I: In memoriam of Adam Kilgarriff; general formalisms; embeddings, language modeling, and sequence labeling; lexical resources and terminology extraction; morphology and part-of-speech tagging; syntax and chunking; named entity recognition; word sense disambiguation and anaphora resolution; semantics, discourse, and dialog. Part II: machine translation and multilingualism; sentiment analysis, opinion mining, subjectivity, and social media; text classification and categorization; information extraction; and applications.

The two-volume set LNCS 9623 + 9624 constitutes revised selected papers from the CICLing 2016 conference which took place in Konya, Turkey, in April 2016. The total of 89 papers presented in the two volumes was carefully reviewed and selected from 298 submissions. The book also contains 4 invited papers and a memorial paper on Adam Kilgarriff's Legacy to Computational Linguistics. The papers are organized in the following topical sections: Part I: In memoriam of Adam Kilgarriff; general formalisms; embeddings, language modeling, and sequence labeling; lexical resources and terminology extraction; morphology and part-of-speech tagging; syntax and chunking; named entity recognition; word sense disambiguation and anaphora resolution; semantics, discourse, and dialog. Part II: machine translation and multilingualism; sentiment analysis, opinion mining, subjectivity, and social media; text classification and categorization; information extraction; and applications.

This book constitutes the refereed proceedings of the 6th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2018, held in Hanoi, Vietnam, in March 2018.The 39 revised full papers presented in this book were carefully reviewed and selected from 76 initial submissions. The papers are organized in topical sections on uncertainty management and decision support; clustering and classification; machine learning applications; statistical methods; and econometric applications.

This book constitutes the refereed proceedings of the 40th European Conference on IR Research, ECIR 2018, held in Grenoble, France, in March 2018. The 39 full papers and 39 short papers presented together with 6 demos, 5 workshops and 3 tutorials, were carefully reviewed and selected from 303 submissions. Accepted papers cover the state of the art in information retrieval including topics such as: topic modeling, deep learning, evaluation, user behavior, document representation, recommendation systems, retrieval methods, learning and classication, and micro-blogs.

This book constitutes the refereed post-conference proceedings of a Workshop focussing on Text Processing, held at the Forum for Information Retrieval Evaluation, FIRE 2016, in Kolkata, India, in December 2016. 16 full papers have been selected for inclusion in the book out of 19 submissions. The papers refer to the following seven tracks: Consumer Health Information Search (CHIS), Detecting Paraphrases in Indian Languages (DPIL), Information Extraction from Microblogs Posted during Disasters, Persian Plagiarism Detection (PersianPlagDet), Personality Recognition in SOurce COde (PR-SOCO), Shared Task on Mixed Script Information Retrieval (MSIR), and Shared Task on Code Mix Entity Extraction in Indian Languages (CMEE-IL).

This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.

This book introduces methods for copyright protection and compression for speech signals. The first method introduces copyright protection of speech signal using watermarking; the second introduces compression of the speech signal using Compressive Sensing (CS). Both methods are tested and analyzed. The speech watermarking method uses technology such as Finite Ridgelet Transform (FRT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The performance of the method is evaluated and compared with existing watermarking methods. In the speech compression method, the standard Compressive Sensing (CS) process is used for compression of the speech signal. The performance of the proposed method is evaluated using various transform bases like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Fast Discrete Curvelet Transform (FDCuT).