The big challenge for a successful AI project isn't deciding which problems you can solve. It's deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features * Selecting the right AI project to meet specific business goals * Economizing resources to deliver the best value for money * How to measure the success of your AI efforts in the business terms * Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It's filled with practical techniques for running data science programs that ensure they're cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

This book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language Processing (NLP). NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems. These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential. Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject. Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.

This book aims to inform researchers with an interest in natural language generation about advances in the field. It is organised around four topics - system architectures, content planning, discourse planning and realisation in linguistic form - and it presents some of the most important works in this area of research.

A central problem of natural language generation is that of 'expressibility'. Meteer presents a solution which uses a level of representation called the Text Structure, an intermediate between the representation of world and the language.

The content of this textbook is organized as a theory of language for the construction of talking robots. The main topic is the mechanism of natural language communication in both the speaker and the hearer. In the third edition the author has modernized the text, leaving the overview of traditional, theoretical, and computational linguistics, analytic philosophy of language, and mathematical complexity theory with their historical backgrounds intact. The format of the empirical analyses of English and German syntax and semantics has been adapted to current practice; and Chaps. 22-24 have been rewritten to focus more sharply on the construction of a talking robot.

ThoughtTreasure is a commonsense knowledge base and architecture for natural language processing. It uses multiple representations including logic, finite automata, grids, and scripts. The ThoughtTreasure architecture consists of: the text agency, containing text agents for recognizing words, phrases, and names, and mechanisms for learning new words and inflections; the syntactic component, containing a syntactic parser, base rules, and filters; the semantic component, containing a semantic parser for producing a surface-level understanding of a sentence, a natural language generator, and an anaphoric parser for resolving anaphoric entities such as pronouns; the planning agency, containing planning agents for achieving goals on behalf of simulated actors; and the understanding agency, containing understanding agents for producing a more detailed understanding of a discourse.

The two-volume set LNCS 13451 and 13452 constitutes revised selected papers from the CICLing 2019 conference which took place in La Rochelle, France, April 2019.The total of 95 papers presented in the two volumes was carefully reviewed and selected from 335 submissions. The book also contains 3 invited papers. The papers are organized in the following topical sections: General, Information extraction, Information retrieval, Language modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining.

This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence and Natural Language, AINL 2022, held in St. Petersburg, Russia, in April 2022. The 8 revised full papers and 1 short paper were carefully reviewed and selected from 20 submissions. The volume presents recent research in areas of of text mining, speech technologies, dialogue systems, information retrieval, machine learning, articial intelligence, and robotics.

This book constitutes revised selected papers from the 23rd International Symposium on Trends in Functional Programming, TFP 2022, which was held virtually in March 2022. The 9 full papers presented in this volume were carefully reviewed and selected from 17 submissions. They deal with all aspects of functional programming, taking a broad view of current and future trends in the area.

This book constitutes the proceedings of the International Joint Conference on Rules and Reasoning, RuleML+RR 2022, held in Berlin, Germany, during September 26-28, 2022. This is the 6th conference of a new series, joining the efforts of two existing conference series, namely "RuleML" (International Web Rule Symposium) and "RR" (Web Reasoning and Rule Systems). The 18 full research papers presented in this book were carefully reviewed and selected from 54 submissions. The papers cover the following topics: answer set programming; foundations of nonmonotonic reasoning; datalog; queries over ontologies; proofs, error-tolerance, and rules; as well as agents and argumentation.

This book constitutes the refereed proceedings of the 24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022, which was held in November/December 2022. The 14 full, 18 short, and 12 poster papers presented in this volume were carefully reviewed and selected from 78 submissions. Based on significant contributions, the full and short papers have been classified into the following topics: intelligent document analysis; neural-based knowledge extraction; knowledge discovery for enhancing collaboration; smart search and annotation; cultural data collection and analysis; scholarly data processing; data archive and management; research activities and digital library; and trends in digital library.

This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have 'linguistics' in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings.

This book constitutes the refereed proceedings of the 18th China Conference on Machine Translation, CCMT 2022, held in Lhasa, China, during August 6-10, 2022. The 16 full papers were included in this book were carefully reviewed and selected from 73 submissions.

This book constitutes the refereed proceedings of the 13th International Conference of the CLEF Association, CLEF 2022, held in Bologna, Italy in September 2022.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 presented together with 3 short papers in this volume were carefully reviewed and selected from 14 submissions. This year, the contributions addressed the following challenges: authorship attribution, fake news detection and news tracking, noise-detection in automatically transferred relevance judgments, impact of online education on children's conversational search behavior, analysis of multi-modal social media content, knowledge graphs for sensitivity identification, a fusion of deep learning and logic rules for sentiment analysis, medical concept normalization and domain-specific information extraction. In addition to this, the volume presents 7 "best of the labs" papers which were reviewed as full paper submissions with the same review criteria. 14 lab overview papers were accepted and represent scientific challenges based on new datasets and real world problems in multimodal and multilingual information access.

This book, with a foreword by Manuel Castells, explores the core strategies of digital political communication. It reviews the field's evolution over the past 25 years and examines the coexistence of old and new actors (lobbyists, citizens, parliaments, political parties, media outlets, digital platforms, among others), as well as hybrid communication tactics. Topics covered include frames, fake news, filter bubbles, echo chambers, artificial intelligence, the significance of emotions, and engagement with citizens.As we find ourselves in the fourth wave of digital communication, and in the wake of a pandemic which has shaken the foundations of political communication, an evaluation of these topics is essential to the reinvention of democracy. The book is geared towards students and researchers who wish to delve into the latest trends in digital communication, political communication actors and journalists. It further aims to prepare citizens to effectively deal with messaging that blurs the line between truth and falsehood with increasingly powerful strategies supported by artificial intelligence.

This book constitutes the refereed proceedings of the 25th Brazilian Symposium on Formal Methods, SBMF 2022, which was held virtually in December 2022. The 8 regular papers presented in this book were carefully reviewed and selected from 15 submissions. The symposium focuses on the development, dissemination, and use of formal methods for the construction of high-quality computational systems, aiming to promote opportunities for researchers and practitioners with an interest in formal methods to discuss the recent advances in this area.

Wineinformatics is a new data science application with a focus on understanding wine through artificial intelligence. Thousands of new wine reviews are produced monthly, which benefits the understanding of wine through wine experts for winemakers and consumers. This book systematically investigates how to process human language format reviews and mine useful knowledge from a large volume of processed data. This book presents a human language processing tool named Computational Wine Wheel to process professional wine reviews and three novel Wineinformatics studies to analyze wine quality, price and reviewers. Through the lens of data science, the author demonstrates how the wine receives 90+ scores out of 100 points from Wine Spectator, how to predict a wine's specific grade and price through wine reviews and how to rank a group of wine reviewers. The book also shows the advanced application of the Computational Wine Wheel to capture more information hidden in wine reviews and the possibility of extending the wheel to coffee, tea beer, sake and liquors. This book targets computer scientists, data scientists and wine industrial researchers, who are interested in Wineinformatics. Senior data science undergraduate and graduate students may also benefit from this book.

This book constitutes the refereed proceedings of the 18th International Conference on Frontiers in Handwriting Recognition, ICFHR 2022, which took place in Hyderabad, India, during December 4-7, 2022. The 36 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 61 submissions. The contributions were organized in topical sections as follows: Historical Document Processing; Signature Verification and Writer Identification; Symbol and Graphics Recognition; Handwriting Recognition and Understanding; Handwriting Datasets and Synthetic Handwriting Generation; Document Analysis and Processing.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.  Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models.  After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23-27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

This book constitutes the thoroughly revised selected papers from the 18th International Symposium, FACS 2022, which was held online in November 2022.The 12 full papers and 1 short paper were carefully reviewed and selected from 25 submissions. FACS 2021 is focusing on the areas of component software and formal methods in order to promote a deeper understanding of how formal methods can or should be used to make component-based software development succeed.

This book constitutes the thoroughly refereed post-workshop proceedings of the 22nd Chinese Lexical Semantics Workshop, CLSW 2021, held in Nanjing, China in May 2021. The 68 full papers and 4 short papers included in this volume were carefully reviewed and selected from 261 submissions. They are organized in the following topical sections: Lexical Semantics and General Linguistics; Natural Language Processing and Language Computing; Cognitive Science and Experimental Studies; Lexical Resources and Corpus Linguistics.

When viewed through a political lens, the act of defining terms in natural language arguably transforms knowledge into values. This unique volume explores how corporate, military, academic, and professional values shaped efforts to define computer terminology and establish an information engineering profession as a precursor to what would become computer science. As the Cold War heated up, U.S. federal agencies increasingly funded university researchers and labs to develop technologies, like the computer, that would ensure that the U.S. maintained economic prosperity and military dominance over the Soviet Union. At the same time, private corporations saw opportunities for partnering with university labs and military agencies to generate profits as they strengthened their business positions in civilian sectors. They needed a common vocabulary and principles of streamlined communication to underpin the technology development that would ensure national prosperity and military dominance. investigates how language standardization contributed to the professionalization of computer science as separate from mathematics, electrical engineering, and physics examines traditions of language standardization in earlier eras of rapid technology development around electricity and radio highlights the importance of the analogy of "the computer is like a human" to early explanations of computer design and logic traces design and development of electronic computers within political and economic contexts foregrounds the importance of human relationships in decisions about computer design This in-depth humanistic study argues for the importance of natural language in shaping what people come to think of as possible and impossible relationships between computers and humans. The work is a key reference in the history of technology and serves as a source textbook on the human-level history of computing. In addition, it addresses those with interests in sociolinguistic questions around technology studies, as well as technology development at the nexus of politics, business, and human relations.

This book constitutes the refereed proceedings of the 12th International Conference on Applications of Natural Language to Information Systems, NLDB 2007, held in Paris, France in June 2007.

The 31 revised full papers and 10 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 110 submissions. The papers are organized in topical sections on natural language for database query processing, email management, semantic annotation, text clustering, ontology engineering, natural language for information system design, information retrieval systems, and natural language processing techniques.