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.

This volume is based on contributions from the First International Conference on “Recent Advances in Natural Language Processing” (RANLP'95) held in Tzigov Chark, Bulgaria, 14-16 September 1995. This conference was one of the most important and competitively reviewed conferences in Natural Language Processing (NLP) for 1995 with submissions from more than 30 countries. Of the 48 papers presented at RANLP'95, the best (revised) papers have been selected for this book, in the hope that they reflect the most significant and promising trends (and latest successful results) in NLP. The book is organised thematically and the contributions are grouped according to the traditional topics found in NLP: morphology, syntax, grammars, parsing, semantics, discourse, grammars, generation, machine translation, corpus processing and multimedia. To help the reader find his/her way, the authors have prepared an extensive index which contains major terms used in NLP; an index of authors which lists the names of the authors and the page numbers of their paper(s); a list of figures; and a list of tables. This book will be of interest to researchers, lecturers and graduate students interested in Natural Language Processing and more specifically to those who work in Computational Linguistics, Corpus Linguistics and Machine Translation.

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 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.

This volume brings together revised versions of a selection of papers presented at the Sixth International Conference on “Recent Advances in Natural Language Processing” (RANLP) held in Borovets, Bulgaria, 27–29 September 2007. These papers cover a wide variety of Natural Language Processing (NLP) topics: ontologies, named entity extraction, translation and transliteration, morphology (derivational and inflectional), part-of-speech tagging, parsing (incremental processing, dependency parsing), semantic role labeling, word sense disambiguation, temporal representations, inference and metaphor, semantic similarity, coreference resolution, clustering (topic modeling, topic tracking), summarization, cross-lingual retrieval, lexical and syntactic resources, multi-modal processing. The aim of this volume is to present new results in NLP based on modern theories and methodologies, making it of interest to researchers in NLP and, more specifically, to those who work in Computational Linguistics, Corpus Linguistics, and Machine Translation.

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