Download or read book Joint Workshop on Unsupervised and Semi Supervised Learning in NLP 2012 ROBUS UNSUP 2012 written by and published by . This book was released on 2012 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of ROBUS UNSUP 2012 written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of the Joint Workshop on Unsupervised and Semi Supervised Learning in NLP written by and published by . This book was released on 2012 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Processing of the Portuguese Language written by Aline Villavicencio and published by Springer. This book was released on 2018-09-14 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Computational Processing of the Portuguese Language, PROPOR 2018, held in Canela, RS, Brazil, in September 2018. The 42 full papers, 3 short papers and 4 other papers presented in this volume were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections named: Corpus Linguistics, Information Extraction, LanguageApplications, Language Resources, Sentiment Analysis and Opinion Mining, Speech Processing, and Syntax and Parsing.
Download or read book Natural Language Processing and Information Systems written by Chris Biemann and published by Springer. This book was released on 2015-06-03 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Advances in Artificial Intelligence and Soft Computing written by Grigori Sidorov and published by Springer. This book was released on 2015-11-19 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNAI 9413 + LNAI 9414 constitutes the proceedings of the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, in October 2015. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 297 submissions. They were organized in topical sections named: natural language processing; logic and multi-agent systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural language processing applications; educational applications; biomedical applications; image processing and computer vision; search and optimization; forecasting; and intelligent applications.
Download or read book Introduction to Natural Language Processing written by Jacob Eisenstein and published by MIT Press. This book was released on 2019-10-01 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
Download or read book An Introduction to Conditional Random Fields written by Charles Sutton and published by Now Pub. This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.
Download or read book Semantic Processing of Legal Texts written by Enrico Francesconi and published by Springer. This book was released on 2010-05-10 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen much new research on the interface between artificial intelligence and law, looking at issues such as automated legal reasoning. This collection of papers represents the state of the art in this fascinating and highly topical field.
Download or read book Recent Advances in Natural Language Processing written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
Download or read book Biomedical Natural Language Processing written by Kevin Bretonnel Cohen and published by John Benjamins Publishing Company. This book was released on 2014-02-15 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining.
Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Download or read book Entity Oriented Search written by Krisztian Balog and published by Springer. This book was released on 2018-10-02 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms.
Download or read book Natural Language Processing as a Foundation of the Semantic Web written by Yorick Wilks and published by Now Publishers Inc. This book was released on 2009 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Looks at how Natural language Processing underpins the Semantic Web, including its initial construction from unstructured sources like the World Wide Web.
Download or read book Conversational Dialogue Systems for the Next Decade written by Luis Fernando D'Haro and published by Springer Nature. This book was released on 2020-10-24 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles and presents a synopsis on current global research efforts to push forward the state of the art in dialogue technologies, including advances to the classical problems of dialogue management, language generation, question answering, human–robot interaction, chatbots design and evaluation, as well as topics related to the human nature of the conversational phenomena such as humour, social context, specific applications for e-health, understanding, and awareness
Download or read book Argumentation Mining written by Manfred Stede and published by Springer Nature. This book was released on 2022-06-01 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.
