This book constitutes the refereed proceedings of the 16th International Conference on Integrated Formal Methods, IFM 2019, held in Lugano, Switzerland, in November 2020. The 24 full papers and 2 short papers were carefully reviewed and selected from 63 submissions. The papers cover a broad spectrum of topics: Integrating Machine Learning and Formal Modelling; Modelling and Verification in B and Event-B; Program Analysis and Testing; Verification of Interactive Behaviour; Formal Verification; Static Analysis; Domain-Specific Approaches; and Algebraic Techniques.

This book deals with "Computer Aided Writing", CAW for short. The contents of that is a sector of Knowledge based technics and Knowledge Management. The role of Knowledge Management in social media, education and Industry 4.0 is out of question. More important is the expectation of combining Knowledge Management and Cognitive Technology, which needs more and more new innovations in this field to face recent problems in social and technological areas. The book is intended to provide an overview of the state of research in this field, show the extent to which computer assistance in writing is already being used and present current research contributions. After a brief introduction into the history of writing and the tools that were created, the current developments are examined on the basis of a formal writing model. Tools such as word processing and content management systems will be discussed in detail. The special form of writing, "journalism", is used to examine the effects of Computer Aided Writing. We dedicate a separate chapter to the topic of research, since it is of essential importance in the writing process. With Knowledge Discovery from Text (KDT) and recommendation systems we enter the field of Knowledge Management in the context of Computer Aided Writing. Finally, we will look at methods for automated text generation before giving a final outlook on future developments.

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

This book focuses mainly on logical approaches to computational linguistics, but also discusses integrations with other approaches, presenting both classic and newly emerging theories and applications.Decades of research on theoretical work and practical applications have demonstrated that computational linguistics is a distinctively interdisciplinary area. There is convincing evidence that computational approaches to linguistics can benefit from research on the nature of human language, including from the perspective of its evolution. This book addresses various topics in computational theories of human language, covering grammar, syntax, and semantics. The common thread running through the research presented is the role of computer science, mathematical logic and other subjects of mathematics in computational linguistics and natural language processing (NLP). Promoting intelligent approaches to artificial intelligence (AI) and NLP, the book is intended for researchers and graduate students in the field.

We intend to edit a Festschrift for Henk Moed combining a "best of" collection of his papers and new contributions (original research papers) by authors having worked and collaborated with him. The outcome of this original combination aims to provide an overview of the advancement of the field in the intersection of bibliometrics, informetrics, science studies and research assessment.

This book constitutes the thoroughly refereed post conference papers of the First International Conference on Blockchain and Trustworthy Systems, Blocksys 2019, held in Guangzhou, China, in December 2019. The 50 regular papers and the 19 short papers were carefully reviewed and selected from 130 submissions. The papers are focus on Blockchain and trustworthy systems can be applied to many fields, such as financial services, social management and supply chain management.

This book constitutes the proceedings of the 23rd International Conference on Developments in Language Theory, DLT 2019, held in Warsaw, Poland, in August 2019. The 20 full papers presented together with three invited talks were carefully reviewed and selected from 30 submissions. The papers cover the following topics and areas: combinatorial and algebraic properties of words and languages; grammars, acceptors and transducers for strings, trees, graphics, 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 bidimensional patterns; cryptography; concurrency; celluar automata; bio-inspired computing; quantum computing.

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. The 21 full papers and 16 short papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: argumentation mining and applications; deep learning, neural languages and NLP; social media and web analytics; question answering; corpus analysis; semantic web, open linked data, and ontologies; natural language in conceptual modeling; natural language and ubiquitous computing; and big data and business intelligence.

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.

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 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.

Globalization has increased the number of individuals in criminal proceedings who are unable to understand the language of the courtroom, and as a result the number of court interpreters has also increased. But unsupervised interpreters can severely undermine the fairness of a criminal proceeding. In this innovative and methodological new study, Dingfelder Stone comprehensively examines the multitudes of mistakes made by interpreters, and explores the resultant legal and practical implications. Whilst scholars of interpreting studies have researched the prevalence of interpreter error for decades, the effect of these mistakes on criminal proceedings has largely gone unanalyzed by legal scholars. Drawing upon both interpreting studies research and legal scholarship alike, this engaging and timely study analyzes the impact of court interpreters on the right to a fair trial under international law, which forms the minimum baseline standard for national systems.

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book's closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Describing the technologies to combine language resources flexibly as web services, this book provides valuable case studies for those who work in services computing, language resources, human-computer interaction (HCI), computer-supported cooperative work (CSCW), and service science. The authors have been operating the Language Grid, which wraps existing language resources as atomic language services and enables users to compose new services by combining them. From architecture level to service composition level, the book explains how to resolve infrastructural and operational difficulties in sharing and combining language resources, including interoperability of language service infrastructures, various types of language service policies, human services, and service failures.The research based on the authors' operating experiences of handling complicated issues such as intellectual property and interoperability of language resources contributes to exploitation of language resources as a service. On the other hand, both the analysis based on using services and the design of new services can bring significant results. A new style of multilingual communication supported by language services is worthy of analysis in HCI/CSCW, and the design process of language services is the focus of valuable case studies in service science. By using language resources in different ways based on the Language Grid, many activities are highly regarded by diverse communities. This book consists of four parts: (1) two types of language service platforms to interconnect language services across service grids, (2) various language service composition technologies that improve the reusability, efficiency, and accuracy of composite services, (3) research work and activities in creating language resources and services, and (4) various applications and tools for understanding and designing language services that well support intercultural collaboration.

This book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language's ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations.

This book constitutes the refereed proceedings of the 12th International Conference, NooJ 2018, held in Palermo, Italy, in June 2018.The 17 revised full papers and 3 short papers presented in this volume were carefully reviewed and selected from 48 submissions. NooJ is a linguistic development environment that provides tools for linguists to construct linguistic resources that formalize a large gamut of linguistic phenomena: typography, orthography, lexicons for simple words, multiword units and discontinuous expressions, inflectional and derivational morphology, local, structural and transformational syntax, and semantics. The papers in this volume are organized in topical sections on vocabulary and morphology; syntax and semantics; and natural language processing applications.

Unraveling the Voynich Codex reviews the historical, botanical, zoological, and iconographic evidence related to the Voynich Codex, one of the most enigmatic historic texts of all time. The bizarre Voynich Codex has often been referred to as the most mysterious book in the world. Discovered in an Italian Catholic college in 1912 by a Polish book dealer Wilfrid Voynich, it was eventually bequeathed to the Beinecke Rare Book and Manuscript Library of Yale University. It contains symbolic language that has defied translation by eminent cryptologists. The codex is encyclopedic in scope and contains sections known as herbal, pharmaceutical, balenological (nude nymphs bathing in pools), astrological, cosmological and a final section of text that may be prescriptions but could be poetry or incantations. Because the vellum has been carbon dated to the early 15th century and the manuscript was known to be in the collection of Emperor Rudolf II of the Holy Roman Empire sometime between 1607 and 1622, current dogma had assumed it a European manuscript of the 15th century. However, based on identification of New World plants, animals, a mineral, as well as cities and volcanos of Central Mexico, the authors of this book reveal that the codex is clearly a document of colonial New Spain. Furthermore, the illustrator and author are identified as native to Mesoamerica based on a name and ligated initials in the first botanical illustration. This breakthrough in Voynich studies indicates that the failure to decipher the manuscript has been the result of a basic misinterpretation of its origin in time and place. Tentative assignment of the Voynichese symbols also provides a key to decipherment based on Mesoamerican languages. A document from this time, free from filter or censor from either Spanish or Inquisitorial authorities has major importance in our understanding of life in 16th century Mexico. Publisher's Note: For the eBook editions, Voynichese symbols are only rendered properly in the PDF format.

This book brings together work on Turkish natural language and speech processing over the last 25 years, covering numerous fundamental tasks ranging from morphological processing and language modeling, to full-fledged deep parsing and machine translation, as well as computational resources developed along the way to enable most of this work. Owing to its complex morphology and free constituent order, Turkish has proved to be a fascinating language for natural language and speech processing research and applications. After an overview of the aspects of Turkish that make it challenging for natural language and speech processing tasks, this book discusses in detail the main tasks and applications of Turkish natural language and speech processing. A compendium of the work on Turkish natural language and speech processing, it is a valuable reference for new researchers considering computational work on Turkish, as well as a one-stop resource for commercial and research institutions planning to develop applications for Turkish. It also serves as a blueprint for similar work on other Turkic languages such as Azeri, Turkmen and Uzbek.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017. The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.

This book constitutes the refereed proceedings of the 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, held in Hamilton, New Zealand, in November 2018. The 20 full, 6 short, and 11 work in progress papers presented in this volume were carefully reviewed and selected from 77 submissions. The papers were organized in topical sections named: topic modeling and semantic analysis; social media, web, and news; heritage and localization; user experience; digital library technology; and use cases and digital librarianship.

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

This book constitutes the thoroughly refereed post conference proceedings of the 4th edition of the Semantic Web Evaluation Challenge, SemWebEval 2018, co-located with the 15th European Semantic Web conference, held in Heraklion, Greece, in June 2018. This book includes the descriptions of all methods and tools that competed at SemWebEval 2018, together with a detailed description of the tasks, evaluation procedures and datasets. The 18 revised full papers presented in this volume were carefully reviewed and selected from 24 submissions. The contributions are grouped in the areas: the mighty storage challenge; open knowledge extraction challenge; question answering over linked data challenge; semantic sentiment analysis.

This book constitutes the proceedings of the 17th China National Conference on Computational Linguistics, CCL 2018, and the 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018, held in Changsha, China, in October 2018. The 33 full papers presented in this volume were carefully reviewed and selected from 84 submissions. They are organized in topical sections named: Semantics; machine translation; knowledge graph and information extraction; linguistic resource annotation and evaluation; information retrieval and question answering; text classification and summarization; social computing and sentiment analysis; and NLP applications.