This book provides the readers with retrospective and prospective views with detailed explanations of component technologies, speech recognition, language translation and speech synthesis. Speech-to-speech translation system (S2S) enables to break language barriers, i.e., communicate each other between any pair of person on the glove, which is one of extreme dreams of humankind. People, society, and economy connected by S2S will demonstrate explosive growth without exception. In 1986, Japan initiated basic research of S2S, then the idea spread world-wide and were explored deeply by researchers during three decades. Now, we see S2S application on smartphone/tablet around the world. Computational resources such as processors, memories, wireless communication accelerate this computation-intensive systems and accumulation of digital data of speech and language encourage recent approaches based on machine learning. Through field experiments after long research in laboratories, S2S systems are being well-developed and now ready to utilized in daily life. Unique chapter of this book is end-2-end evaluation by comparing system's performance and human competence. The effectiveness of the system would be understood by the score of this evaluation. The book will end with one of the next focus of S2S will be technology of simultaneous interpretation for lecture, broadcast news and so on.

This book constitutes the refereed proceedings of the 15th China Conference on Machine Translation, CCMT 2019, held in Nanchang, China, in September 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 21 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG). This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

This book constitutes the refereed proceedings of the 13th International Conference on Mathematics of Program Construction, MPC 2019, held in Porto, Portugal, in October 2019. The 15 revised full papers presented together with an invited paper were carefully reviewed and selected from 22 submissions. The papers deal with mathematical principles and techniques for constructing computer programs. They range from algorithmics to support for program construction in programming languages and systems. Some typical areas are type systems, program analysis and transformation, programming-language semantics, security, and program logics.

This book constitutes the proceedings of the 18th China National Conference on Computational Linguistics, CCL 2019, held in Kunming, China, in October 2019. The 56 full papers presented in this volume were carefully reviewed and selected from 134 submissions. They were organized in topical sections named: linguistics and cognitive science, fundamental theory and methods of computational linguistics, information retrieval and question answering, text classification and summarization, knowledge graph and information extraction, machine translation and multilingual information processing, minority language processing, language resource and evaluation, social computing and sentiment analysis, NLP applications.

This volume LNCS 11877 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2019, Ontologies, Databases, and Applications of Semantics, ODBASE 2019, and Cloud and Trusted Computing, C&TC, held as part of OTM 2019 in October 2019 in Rhodes, Greece. The 38 full papers presented together with 8 short papers were carefully reviewed and selected from 156 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, informationsystems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.

This updated book expands upon prosody for recognition applications of speech processing. It includes importance of prosody for speech processing applications; builds on why prosody needs to be incorporated in speech processing applications; and presents methods for extraction and representation of prosody for applications such as speaker recognition, language recognition and speech recognition. The updated book also includes information on the significance of prosody for emotion recognition and various prosody-based approaches for automatic emotion recognition from speech.

This book constitutes the refereed proceedings of the 25th China Conference on Information Retrieval, CCIR 2019, held in Fuzhou, China, in September 2019. The 13 full papers presented were carefully reviewed and selected from 45 submissions. Academic research is aimed at the precise acquisition of information and knowledge by human beings. The research results will support national strategic decision-making, promote the development of the Internet and IT fields, enhance the production efficiency of the industry, and have a major impact on various fields of social life.

This two volume set constitutes the refereed proceedings of the 6th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2018, held as part of the 20th International Conference on Human-Computer Interaction, HCII 2018, held in Las Vegas, NV, USA in July 2018. The total of 1171 papers and 160 posters presented at the 14 colocated HCII 2018 conferences. The papers were carefully reviewed and selected from 4346 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.. TheLNCS 10921 and LNCS 10922 contains papers addressing the following major topics: Technologies and Contexts ( Part I) and Understanding Humans (Part I )

This book constitutes revised selected papers from the 7th International Conference on Arabic Language Processing, ICALP 2019, held in Nancy, France, in October 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 38 submissions. They were organized in topical sections named: Arabic dialects and sentiment analysis; neural techniques for text and speech; modeling modern standard Arabic; resources: analysis, disambiguation and evaluation.

This book constitutes the refereed proceedings of the 10th International Conference of the CLEF Association, CLEF 2019, held in Lugano, Switzerland, in September 2019.The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 7 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 30 submissions. This year, many contributions tackle the social networks with the detection of stances or early identification of depression signs on Twitter in a cross-lingual context. Further this volume presents 7 "best of the labs" papers which were reviewed as a full paper submission with the same review criteria. The labs represented scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. In addition to this, 9 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions.

The European Summer School in Logic, Language and Information (ESSLLI) is organized every year by the Association for Logic, Language and Information (FoLLI) in different sites around Europe. The papers cover vastly dierent topics, but each fall in the intersection of the three primary topics of ESSLLI: Logic, Language and Computation. The 14 papers presented in this volume have been selected among 24 papers presented by talks or posters at the Student Sessions of the 30th edition of ESSLLI, held in 2018 in Sofia, Bulgaria.The Student Session is a forum for PhD and Master students to present their research at the interfaces of logic, language and computation. It features three tracks: Logic and Computation (LoCo), Logic and Language (LoLa), and Language and Computation (LaCo).

This book constitutes the proceedings of the 15th International Symposium on Bioinformatics Research and Applications, ISBRA 2019, held in Barcelona, Spain, in June 2019. The 22 full papers presented in this book were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data analysis and methodology.

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.

This book constitutes the refereed proceedings of the 16th International Semantic Web Conference, ESWC 2019, held in Portoroz, Slovenia. The 39 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in three tracks: research track, resources track, and in-use track and deal with the following topical areas: distribution and decentralisation, velocity on the Web, research of research, ontologies and reasoning, linked data, natural language processing and information retrieval, semantic data management and data infrastructures, social and human aspects of the Semantic Web, and, machine learning.

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.

The two-volume set LNBI 11465 and LNBI 11466 constitutes the proceedings of the 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019, held in Granada, Spain, in May 2019. The total of 97 papers presented in the proceedings, was carefully reviewed and selected from 301 submissions. The papers are organized in topical sections as follows: Part I: High-throughput genomics: bioinformatics tools and medical applications; omics data acquisition, processing, and analysis; bioinformatics approaches for analyzing cancer sequencing data; next generation sequencing and sequence analysis; structural bioinformatics and function; telemedicine for smart homes and remote monitoring; clustering and analysis of biological sequences with optimization algorithms; and computational approaches for drug repurposing and personalized medicine. Part II: Bioinformatics for healthcare and diseases; computational genomics/proteomics; computational systems for modelling biological processes; biomedical engineering; biomedical image analysis; and biomedicine and e-health.

This book constitutes the refereed proceedings of the First International Conference on Intelligent Technologies and Applications, INTAP 2018, held in Bahawalpur, Pakistan, in October 2018. The 68 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 251 submissions. The papers of this volume are organized in topical sections on AI and health; sentiment analysis; intelligent applications; social media analytics; business intelligence;Natural Language Processing; information extraction; machine learning; smart systems; semantic web; decision support systems; image analysis; automated software engineering.

This book presents multibiometric watermarking techniques for security of biometric data. This book also covers transform domain multibiometric watermarking techniques and their advantages and limitations. The authors have developed novel watermarking techniques with a combination of Compressive Sensing (CS) theory for the security of biometric data at the system database of the biometric system. The authors show how these techniques offer higher robustness, authenticity, better imperceptibility, increased payload capacity, and secure biometric watermarks. They show how to use the CS theory for the security of biometric watermarks before embedding into the host biometric data. The suggested methods may find potential applications in the security of biometric data at various banking applications, access control of laboratories, nuclear power stations, military base, and airports.

This book discusses some of the basic issues relating to corpus generation and the methods normally used to generate a corpus. Since corpus-related research goes beyond corpus generation, the book also addresses other major topics connected with the use and application of language corpora, namely, corpus readiness in the context of corpus sanitation and pre-editing of corpus texts; the application of statistical methods; and various text processing techniques. Importantly, it explores how corpora can be used as a primary or secondary resource in English language teaching, in creating dictionaries, in word sense disambiguation, in various language technologies, and in other branches of linguistics. Lastly, the book sheds light on the status quo of corpus generation in Indian languages and identifies current and future needs. Discussing various technical issues in the field in a lucid manner, providing extensive new diagrams and charts for easy comprehension, and using simplified English, the book is an ideal resource for non-native English readers. Written by academics with many years of experience teaching and researching corpus linguistics, its focus on Indian languages and on English corpora makes it applicable to graduate and postgraduate students of applied linguistics, computational linguistics and language processing in South Asia and across countries where English is spoken as a first or second language.

This book presents a statistical parametric speech synthesis (SPSS) framework for developing a speech synthesis system where the desired speech is generated from the parameters of vocal tract and excitation source. Throughout the book, the authors discuss novel source modeling techniques to enhance the naturalness and overall intelligibility of the SPSS system. This book provides several important methods and models for generating the excitation source parameters for enhancing the overall quality of synthesized speech. The contents of the book are useful for both researchers and system developers. For researchers, the book is useful for knowing the current state-of-the-art excitation source models for SPSS and further refining the source models to incorporate the realistic semantics present in the text. For system developers, the book is useful to integrate the sophisticated excitation source models mentioned to the latest models of mobile/smart phones.

The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

This book constitutes the refereed proceedings of the 14th China Workshop on Machine Translation, CWMT 2018, held in Wuyishan, China, in October 2018. The 9 papers presented in this volume were carefully reviewed and selected from 17 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

This book applies formal language and automata theory in the context of Tibetan computational linguistics; further, it constructs a Tibetan-spelling formal grammar system that generates a Tibetan-spelling formal language group, and an automata group that can recognize the language group. In addition, it investigates the application technologies of Tibetan-spelling formal language and automata. Given its creative and original approach, the book offers a valuable reference guide for researchers, teachers and graduate students in the field of computational linguistics.

This book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence. Today, many disciplines require automatic text analysis based on the syntactic and semantic characteristics of language and as such several techniques for parsing sentences have been proposed. Which is better? In this book the authors begin with simple heuristics before moving on to more complex methods that identify nouns and verbs and then aggregate modifiers, and lastly discuss methods that can handle complex subordinate and relative clauses. During this process, several ambiguities arise. SP are commonly determined on the basis of the association between a pair of words. However, in many cases, SP depend on more words. For example, something (such as grass) may be edible, depending on who is eating it (a cow?). Moreover, things such as popcorn are usually eaten at the movies, and not in a restaurant. The authors deal with these phenomena from different points of view.