Neural Network Methods in Natural Language Processing

Neural Network Methods in Natural Language Processing Author Yoav Goldberg
ISBN-10 9781627052955
Release 2017-04-17
Pages 309
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Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.



Handbook of Natural Language Processing

Handbook of Natural Language Processing Author Robert Dale
ISBN-10 0824790006
Release 2000-07-25
Pages 964
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This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.



The Handbook of Computational Linguistics and Natural Language Processing

The Handbook of Computational Linguistics and Natural Language Processing Author Alexander Clark
ISBN-10 9781118448670
Release 2013-04-24
Pages 650
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This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies



Neural Networks for Vision Speech and Natural Language

Neural Networks for Vision  Speech and Natural Language Author R. Linggard
ISBN-10 9789401123600
Release 2012-12-06
Pages 442
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This book is a collection of chapters describing work carried out as part of a large project at BT Laboratories to study the application of connectionist methods to problems in vision, speech and natural language processing. Also, since the theoretical formulation and the hardware realization of neural networks are significant tasks in themselves, these problems too were addressed. The book, therefore, is divided into five Parts, reporting results in vision, speech, natural language, hardware implementation and network architectures. The three editors of this book have, at one time or another, been involved in planning and running the connectionist project. From the outset, we were concerned to involve the academic community as widely as possible, and consequently, in its first year, over thirty university research groups were funded for small scale studies on the various topics. Co-ordinating such a widely spread project was no small task, and in order to concentrate minds and resources, sets of test problems were devised which were typical of the application areas and were difficult enough to be worthy of study. These are described in the text, and constitute one of the successes of the project.



Learning to Rank for Information Retrieval and Natural Language Processing

Learning to Rank for Information Retrieval and Natural Language Processing Author Hang Li
ISBN-10 9781627055857
Release 2014-10-01
Pages 121
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Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work



Natural Language Processing with Modular Neural Networks and Distributed Lexicon

Natural Language Processing with Modular Neural Networks and Distributed Lexicon Author Risto Miikkulainen
ISBN-10 OCLC:22517066
Release 1990
Pages 26
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The recurrent FGREP module, together with a central lexicon, is used as a basic building block in modeling higher-level natural language tasks. A single module is used to form case-role representations of sentences from word-by-word sequential natural language input. A hierarchial organization of four modules is trained to produce fully expanded paraphrases of script-based stories, where unmentioned event and role fillers are inferred."



Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data Author Maosong Sun
ISBN-10 9783319476742
Release 2016-10-09
Pages 460
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This book constitutes the proceedings of the 15th China National Conference on Computational Linguistics, CCL 2016, and the 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, held in Yantai City, China, in October 2016. The 29 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named: semantics; machine translation; multilinguality in NLP; 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.



Connectionist Statistical and Symbolic Approaches to Learning for Natural Language Processing

Connectionist  Statistical and Symbolic Approaches to Learning for Natural Language Processing Author Stefan Wermter
ISBN-10 3540609253
Release 1996-03-15
Pages 474
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This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.



New Methods In Language Processing

New Methods In Language Processing Author D. B. Jones
ISBN-10 9781134227389
Release 2013-11-05
Pages 385
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Studies in Computational Linguistics presents authoritative texts from an international team of leading computational linguists. The books range from the senior undergraduate textbook to the research level monograph and provide a showcase for a broad range of recent developments in the field. The series should be interesting reading for researchers and students alike involved at this interface of linguistics and computing.



Neural Network Perspectives on Cognition and Adaptive Robotics

Neural Network Perspectives on Cognition and Adaptive Robotics Author A Browne
ISBN-10 0750304553
Release 1997-01-01
Pages 270
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Featuring an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to the modeling of human cognition and language using neural computing techniques. It also describes how adaptive robotic systems can be produced using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a different perspective.



Advances in Neural Networks ISNN 2005

Advances in Neural Networks   ISNN 2005 Author Jun Wang
ISBN-10 9783540259145
Release 2005-05-17
Pages 1077
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This book and its sister volumes constitute the proceedings of the 2nd International Symposium on Neural Networks (ISNN 2005). ISNN 2005 was held in the beautiful mountain city Chongqing by the upper Yangtze River in southwestern China during May 30-June 1, 2005, as a sequel of ISNN 2004 successfully held in Dalian, China. ISNN emerged as a leading conference on neural computation in the region with - creasing global recognition and impact. ISNN 2005 received 1425 submissions from authors on ?ve continents (Asia, Europe, North America, South America, and Oc- nia), 33 countries and regions (Mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, India, Nepal, Iran, Qatar, United Arab Emirates, Turkey, Lithuania, Hungary, Poland, Austria, Switzerland, Germany, France, Sweden, Norway, Spain, Portugal, UK, USA, Canada, Venezuela, Brazil, Chile, Australia, and New Zealand). Based on rigorous reviews, 483 high-quality papers were selected by the Program Committee for presentation at ISNN 2005 and publication in the proce- ings, with an acceptance rate of less than 34%. In addition to the numerous contributed papers, 10 distinguished scholars were invited to give plenary speeches and tutorials at ISNN 2005.



Proceedings of the International Conference on Natural Language Processing ICON 2005

Proceedings of the International Conference on Natural Language Processing  ICON  2005 Author
ISBN-10 8177649604
Release 2005
Pages 119
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Contributed papers presented at the 2005 International Conference, held at IIT Kanpur, organized by NLP Association of India, etc.



Information Retrieval in Biomedicine Natural Language Processing for Knowledge Integration

Information Retrieval in Biomedicine  Natural Language Processing for Knowledge Integration Author Prince, Violaine
ISBN-10 9781605662756
Release 2009-03-31
Pages 460
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"This book provides relevant theoretical frameworks and the latest empirical research findings in biomedicine information retrieval as it pertains to linguistic granularity"--Provided by publisher.



Artificial Neural Networks ICANN 96

Artificial Neural Networks   ICANN 96 Author Christoph von der Malsburg
ISBN-10 3540615105
Release 1996-07-10
Pages 922
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This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.



Proceedings of the Conference on Empirical methods in Natural Language Processing

Proceedings of the Conference on Empirical methods in Natural Language Processing Author Association for Computational Linguistics
ISBN-10 CORNELL:31924091021885
Release 1996
Pages 152
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Proceedings of the Conference on Empirical methods in Natural Language Processing has been writing in one form or another for most of life. You can find so many inspiration from Proceedings of the Conference on Empirical methods in Natural Language Processing also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Proceedings of the Conference on Empirical methods in Natural Language Processing book for free.



Intelligent Natural Language Processing Trends and Applications

Intelligent Natural Language Processing  Trends and Applications Author Khaled Shaalan
ISBN-10 9783319670560
Release 2017-11-17
Pages 776
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This book brings together scientists, researchers, practitioners, and students from academia and industry to present recent and ongoing research activities concerning the latest advances, techniques, and applications of natural language processing systems, and to promote the exchange of new ideas and lessons learned. Taken together, the chapters of this book provide a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of intelligent natural language processing. The book presents the state-of-the-art in research on natural language processing, computational linguistics, applied Arabic linguistics and related areas. New trends in natural language processing systems are rapidly emerging – and finding application in various domains including education, travel and tourism, and healthcare, among others. Many issues encountered during the development of these applications can be resolved by incorporating language technology solutions. The topics covered by the book include: Character and Speech Recognition; Morphological, Syntactic, and Semantic Processing; Information Extraction; Information Retrieval and Question Answering; Text Classification and Text Mining; Text Summarization; Sentiment Analysis; Machine Translation Building and Evaluating Linguistic Resources; and Intelligent Language Tutoring Systems.



Big Data Analytics Methods

Big Data Analytics Methods Author Peter Ghavami
ISBN-10 1530414830
Release 2016-03-06
Pages 304
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Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensemble of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods are covered. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. This book is ideal as a text book for a course or as a reference for data scientists, data engineers, data analysts, Business intelligence practitioners, and business managers. It covers 10 chapters that discuss natural language processing (NLP), data visualization, prediction, optimization, artificial intelligence, regression analysis, cox hazard model and many analytics use case examples with applications in healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services. Big Data Analytics Methods Is a must read for those who wish to gain confidence and knowledge about big data and advanced analytics techniques. Read this book and confidently speak, lead and guide others about machine learning, neural networks, NLP, deep learning, and over 100 other analytics techniques. This book is fun and easy to read. It starts with simple and broad explanation of methods and gradually introduces more technical terms and techniques layer by layer. It finally introduces the underlying mathematical terms for those who want a mathematical foundation of the analytics methods. This book is one of a kind as it provides state of the art in advanced data analytics methods with important best practices to ensure the reader's success in data analytics.