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Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 9781588116185
Category : Language Arts & Disciplines
Languages : en
Pages : 420
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Book Description
This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on "Recent Advances in Natural Language Processing". A wide range of topics is covered in the volume: semantics, dialog, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various 'state-of-the-art' techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics.
Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 9781588116185
Category : Language Arts & Disciplines
Languages : en
Pages : 420
Get Book Here
Book Description
This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on "Recent Advances in Natural Language Processing". A wide range of topics is covered in the volume: semantics, dialog, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various 'state-of-the-art' techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics.
Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 9027294682
Category : Language Arts & Disciplines
Languages : en
Pages : 418
Get Book Here
Book Description
This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on “Recent Advances in Natural Language Processing”. A wide range of topics is covered in the volume: semantics, dialogue, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various ‘state-of-the-art’ techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics.
Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 902723695X
Category : Language Arts & Disciplines
Languages : en
Pages : 435
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Book Description
This volume brings together revised versions of a selection of papers presented at the Second International Conference on Recent Advances in Natural Language Processing (RANLP'97) held in Tzigov Chark, Bulgaria, September 1997. The aim of the conference was to give researchers the opportunity to present new results in Natural Language Processing (NLP) based both on traditional and modern theories and approaches. The conference received substantial interest 167 submissions from more than 20 countries. The best papers from the proceedings were selected for this volume, in the hope that they reflect the most significant and promising trends (and successful results) in NLP. The contributions have been grouped according to the following topics: tagging, lexical issues and parsing, word sense disambiguation and anaphora resolution, semantics, generation, machine translation, and categorisation and applications. The volume contains an extensive index.
Author: Adam Przepiórkowski
Publisher: Springer
ISBN: 3319108883
Category : Computers
Languages : en
Pages : 506
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Advances in Natural Language Processing, PolTAL 2014, Warsaw, Poland, in September 2014. The 27 revised full papers and 20 revised short papers presented were carefully reviewed and selected from 83 submissions. The papers are organized in topical sections on morphology, named entity recognition, term extraction; lexical semantics; sentence level syntax, semantics, and machine translation; discourse, coreference resolution, automatic summarization, and question answering; text classification, information extraction and information retrieval; and speech processing, language modelling, and spell- and grammar-checking.
Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 9027248257
Category : Language Arts & Disciplines
Languages : en
Pages : 353
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Book Description
Linguistics, Corpus Linguistics, and Machine Translation." --Book Jacket.
Author: Zhiyuan Liu
Publisher: Springer Nature
ISBN: 9811555737
Category : Computers
Languages : en
Pages : 319
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Book Description
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Author: Bandyopadhyay, Sivaji
Publisher: IGI Global
ISBN: 1466621702
Category : Computers
Languages : en
Pages : 389
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Book Description
"This book provides pertinent and vital information that researchers, postgraduate, doctoral students, and practitioners are seeking for learning about the latest discoveries and advances in NLP methodologies and applications of NLP"--Provided by publisher.
Author: Paul Azunre
Publisher: Simon and Schuster
ISBN: 163835099X
Category : Computers
Languages : en
Pages : 262
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Book Description
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
Author: Nicolas Nicolov
Publisher: John Benjamins Publishing
ISBN: 9027283974
Category : Language Arts & Disciplines
Languages : en
Pages : 436
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Book Description
This volume brings together revised versions of a selection of papers presented at the Second International Conference on “Recent Advances in Natural Language Processing” (RANLP’97) held in Tzigov Chark, Bulgaria, September 1997. The aim of the conference was to give researchers the opportunity to present new results in Natural Language Processing (NLP) based both on traditional and modern theories and approaches. The conference received substantial interest — 167 submissions from more than 20 countries. The best papers from the proceedings were selected for this volume, in the hope that they reflect the most significant and promising trends (and successful results) in NLP. The contributions have been grouped according to the following topics: tagging, lexical issues and parsing, word sense disambiguation and anaphora resolution, semantics, generation, machine translation, and categorisation and applications. The volume contains an extensive index.
Author: Mohammad Taher Pilehvar
Publisher: Morgan & Claypool Publishers
ISBN: 1636390226
Category : Computers
Languages : en
Pages : 177
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Book Description
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
