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• CHAPTER 1 GLOBAL TRENDS
• 121.1
• History of Natural Language Processing 13
• 1.1.1 The Early Stage 13
• 1.1.2 The Golden Years of Heuristics and Expert Systems
• 15
• 1.1.3 The Rise of Statistical Machine Learning
• 20
• 1.1.4 The Heyday Dominated by Deep Learning
• 23
• 1.2
• Development of NLP Technology
• 25
• 1.2.1 Recent Boom of NLP 25
• 1.2.2 Characteristics of NLP Technologies
• 28
• 1.3
• Industrial Applications of NLP 33
• 1.3.1 Industrial-scale Production
• 33
• 1.3.2 Diversified NLP Applications 34
• 1.3.3 Open-Source Platform and Ecosystem Accelerating Industrial Intelligent Transformation 38
• CHAPTER 2 DEVELOPMENT STATUS IN CHINA 42
• 2.1
• Top Planning and Overall Layout to Create Strategic Environment for Innovative Development 42
• 2.2
• Overall Equivalent to The World Advanced Level
• 43
• 2.2.1 The State of the Overall Development 43
• 2.2.2 Development Status of Key Technologies
• 48
• 2.3
• Strong Market Demands Industry Entities to Expand
• 50
• CHAPTER 3 TRENDS AND HIGHLIGHTS IN CHINA
• 54
• 3.1
• Knowledge Graph
• 54
• 3.1.1 Development and Hot Topics of KG
• 54
• 3.1.2 Hot Industrial Applications
• 59
• 3.1.3 Trends and Prospects 63
• 3.2
• Language Understanding
• 64
• 3.2.1 Technology Highlights 64
• 3.2.2 Application Highlights 70
• 3.2.3 Trends and Prospects 73
• 3.3
• Language Generation 74
• 3.3.1 Technology Highlights 75
• 3.3.2 Application Highlights 78
• 3.3.3 Trends and Prospects 80
• 3.4
• Intelligent Search
• 82
• 3.4.1 Technology Highlights 82
• 3.4.2 Application Highlights 87
• 3.4.3 Trends and Prospects 89
• 3.5
• Machine Translation 90
• 3.5.1 Technology Highlights 90
• 3.5.2 Application Highlights 94
• 3.5.3 Trends and Prospects 95
• 3.6
• Dialogue System
• 97
• 3.6.1 Technology Highlights 97
• 3.6.2 Application Highlights 104
• 3.6.3 Trends and Prospects 110
• CHAPTER 4 TRENDS AND PROSPECTS 112
• 4.1
• Challenges
• 112
• 4.2
• Future Directions
• 113
• 4.3
• Industry Application Focus
• 114.
• (source: Nielsen Book Data)

This book is a part of the Blue Book series "Research on the Development of Electronic Information Engineering Technology in China", which explores the cutting edge of natural language processing (NLP) studies. The research objects of natural language processing are evolved from words, phrases, and sentences to text, and research directions are from language analysis, language understanding, language generation, knowledge graphs, machine translation, to deep semantic understanding, and beyond. This is in line with the development trend of applications. And for another typical NLP application machine translation, from text translation, to voice and image translation, now simultaneous interpretation, progress of technology makes the application of machine translation deeper and wider into diverse industries. This book is intended for researchers and industrial staffs who have been following the current situation and future trends of the natural language processing. Meanwhile, it also bears high value of reference for experts, scholars, and technical and engineering managers of different levels and different fields.
(source: Nielsen Book Data)

• 1. A primer on transformers
• 2. Understanding the BERT model
• 3. Getting hands-on with BERT
• 4. BERT variants I
• 5. BERT variants II
• 6. Exploring BERTSUM for text summarization
• 7. Applying BERT to other languages
• 8. Exploring sentence and domain-specific BERT
• 9. Working with VideoBERT, BART, and more.

Getting Started with Google BERT will help you become well-versed with the BERT model from scratch and learn how to create interesting NLP applications. You'll understand several variants of BERT such as ALBERT, RoBERTa, DistilBERT, ELECTRA, VideoBERT, and many others in detail.

• Table of Contents A Primer on Transformer Model Understanding the BERT Model Getting Hands-On with BERT BERT variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT BERT variants II - Based on knowledge distillation Exploring BERTSUM for Text Summarization Applying BERT for Other Languages Exploring Sentence and Domain Specific BERT Working with VideoBERT, BART, and more.
• (source: Nielsen Book Data)

Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library Key Features Explore the encoder and decoder of the transformer model Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT Discover how to pre-train and fine-tune BERT models for several NLP tasks Book DescriptionBERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer's encoder and decoder work. You'll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you'll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you'll be well-versed with using BERT and its variants for performing practical NLP tasks. What you will learn Understand the transformer model from the ground up Find out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasks Get hands-on with BERT by learning to generate contextual word and sentence embeddings Fine-tune BERT for downstream tasks Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models Get the hang of the BERT models based on knowledge distillation Understand cross-lingual models such as XLM and XLM-R Explore Sentence-BERT, VideoBERT, and BART Who this book is forThis book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.
(source: Nielsen Book Data)

• Part I: Foundation
• 1. Introduction to Machine Reading Comprehension
• 2. The Basics of Natural Language Processing
• 3. Deep Learning in Natural Language Processing Part II: Architecture
• 4. Architecture of MRC Models
• 5. Common MRC Models
• 6. Pre-trained Language Model Part III: Application
• 7. Code Analysis of SDNet Model
• 8. Applications and Future of Machine Reading Comprehension Appendix A. Machine Learning Basics B. Deep Learning Basics.
• (source: Nielsen Book Data)

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing.
(source: Nielsen Book Data)

• Part I: Foundation
• 1. Introduction to Machine Reading Comprehension
• 2. The Basics of Natural Language Processing
• 3. Deep Learning in Natural Language Processing Part II: Architecture
• 4. Architecture of MRC Models
• 5. Common MRC Models
• 6. Pre-trained Language Model Part III: Application
• 7. Code Analysis of SDNet Model
• 8. Applications and Future of Machine Reading Comprehension Appendix A. Machine Learning Basics B. Deep Learning Basics.
• (source: Nielsen Book Data)

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing.
(source: Nielsen Book Data)

• Table of Contents From Bag-of-Words to the Transformers A Hands-On Introduction to the Subject Autoencoding Language Models Autoregressive and Other Language Models Fine-Tuning Language Models for Text Classification Fine-Tuning Language Models for Token Classification Text Representation Working with Efficient Transformers Cross-Lingual and Multilingual Language Modeling Serving Transformer Models Attention Visualization and Experiment Tracking.
• (source: Nielsen Book Data)

Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models. What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is forThis book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Statistical Hypothesis Testing Statistical Significance Tests Statistical Significance in NLP Deep Significance Replicability Analysis Open Questions and Challenges Conclusions Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
(source: Nielsen Book Data)

• Full Papers.- Audio Features, Precomputed for Podcast Retrieval and Information Access Experiments.- A Hybrid Approach for Stock Market Prediction Using Financial News and Stocktwits.- Dataset Creation Framework for Personalized Type-based Facet Ranking Tasks Evaluation.- SubjectivITA: An Italian Corpus for Subjectivity Detection in Newspapers.- Evaluating Elements of Web-based Data Enrichment for Pseudo-Relevance Feedback Retrieval.- End-to-end Fine-grained Neural Entity Recognition of Patients, Interventions, and Outcomes.- Comparing Traditional and Neural Approaches for detecting Health-related Misinformation.- Towards the evaluation of Information Retrieval Systems on evolving datasets with pivot systems.- A Multi-Task Learning Model for Multidimensional Relevance Assessment.- Skill Extraction for Domain-Specific Text Retrieval in a Job-Matching Platform.- Linguistic Uncertainty in Clinical NLP: a Taxonomy, Dataset and Approach.- Best of 2020 Labs.- Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift.- Priberam at MESINESP Multi-label Classification of Medical Texts Task.- Herbarium-Field Triplet Network for Cross-Domain Plant Identification.- BERT-based Transformers for Early Detection of Mental Health Illnesses.- Dowsing for Math Answers.- Overviews 2021 Labs.- Overview of ARQMath-2 (2021): Second CLEFLab on Answer Retrieval for Questions on Math.- Overview of BioASQ
• 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering.- The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News.- Overview of ChEMU
• 2021: Reaction Reference Resolution and Anaphora Resolution in Chemical Patents.- Overview of the CLEF eHealth Evaluation Lab 2021.- Overview of eRisk
• 2021: Early Risk Prediction on the Internet.- Overview of the ImageCLEF
• 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications.- Overview of LifeCLEF
• 2021: an Evaluation of Machine-Learning based Species Identification and Species Distribution Prediction.- Overview of LiLAS 2021 - Living Labs for Academic Search.- Overview of PAN
• 2021: Authorship Verification, Pro ling Hate Speech Spreaders on Twitter, and Style Change Detection.- Overview of SimpleText 2021 - CLEF Workshop on Text Simplification for Scientific Information Access.- Overview of Touch e
• 2021: Argument Retrieval.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 12th International Conference of the CLEF Association, CLEF 2021, held virtually in September 2021.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 11 full papers presented in this volume were carefully reviewed and selected from 21 submissions. This year, the contributions addressed the following challenges: application of neural methods for entity recognition as well as misinformation detection in the health area, skills extraction in job-match databases, stock market prediction using financial news, and extraction of audio features for podcast retrieval. In addition to this, the volume presents 5 "best of the labs" papers which were reviewed as full paper submissions with the same review criteria. 12 lab overview papers were accepted and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access.
(source: Nielsen Book Data)

• Linguistic Formalization.- Digital Humanities and Teaching with NooJ.- Natural Language Processing Applications.
• (source: Nielsen Book Data)

This book constitutes selected revised papers of the 14th International Conference, NooJ 2020, held Zagreb, Croatia, in June 2020. Due to the COVID-19 pandemic the conference was held online. NooJ is a linguistic development environment that allows linguists to formalize several levels of linguistic phenomena. NooJ provides linguists with tools to develop dictionaries, regular grammars, context-free grammars, context-sensitive grammars and unrestricted grammars as well as their graphical equivalent to formalize each linguistic phenomenon. The 20 full papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in the following topics: Linguistic Formalization; Digital Humanities and Teaching with NooJ; Natural Language Processing Applications. .
(source: Nielsen Book Data)

• The Role of Learning
• You can't Learn what's not there: Self Supervised Learning and the Poverty of the Stimulus
• Scaling Federated Learning for Fine-tuning of Large Language Models
• Overcoming the Knowledge Bottleneck Using Lifelong Learning by Social Agents
• Methodological Approaches
• Word Embedding-based Topic Similarity Measures
• Mixture Variational Autoencoder of Boltzmann Machines for Text Processing
• A Modular Approach for Romanian-English Speech Translation
• NumER: A Fine-grained Numeral Entity Recognition Dataset
• Cross-Domain Transfer of Generative Explanations using Text-to-Text Models
• Semantic Relations
• Virus Causes Flu: Identifying Causality in the Biomedical Domain using an Ensemble Approach with Target-specific Semantic Embeddings
• Multilevel Entity-Informed Business Relation Extraction
• The Importance of Character-Level Information in an Event Detection Model
• Classification
• Sequence-based Word Embeddings for Effective Text Classification
• BERT-Capsule Model for Cyberbullying Detection in Code-Mixed Indian Languages
• Multiword Expression Features for Automatic Hate Speech Detection
• Semantic Text Segment Classification of Structured Technical Content
• On the Generalization of Figurative Language Detection: the Case of Irony and Sarcasm
• Extracting Facts from Case Rulings through Paragraph Segmentation of Judicial Decisions
• Detection of Misinformation about COVID-19 in Brazilian Portuguese WhatsApp Messages
• Sentiment Analysis
• Multi-Step Transfer Learning for Sentiment Analysis
• Improving Sentiment Classification in Low Resource Bengali Language Utilizing Cross-lingual Self-supervised Learning
• Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting
• Exploring Summarization to Enhance Headline Stance Detection
• Predicting Vaccine Hesitancy and Vaccine Sentiment using Topic Modeling and Evolutionary Optimization
• Sentiment Progression based Searching and Indexing of Literary Textual Artefacts
• Social Media
• Argument Mining in Tweets: Comparing Crowd and Expert Annotations for Automated Claim and Evidence Detection
• Authorship Attribution using Capsule-based Fusion Approach
• On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data
• Linking Documents
• Using Document Embeddings for Background Linking of News Articles
• Let's Summarize Scientific Documents! A Clustering-based Approach via Citation Context
• Multimodality
• Cross-Active Connection for Image-Text Multimodal Feature Fusion
• Profiling Fake News Spreaders: Personality and Visual Information Matter 315
• Applications
• Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling
• Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements.

This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications.

• Text-Independent Speaker Verification Employing CNN-LSTM-TDNN Hybrid Networks.- End-to-End Voice Spoofing Detection Employing Time Delay Neural Networks and Higher Order Statistics.- Assessing Velar Gestures Timing in European Portuguese Nasal Vowels with RT-MRI Data.- Designing and Deploying an Interaction Modality for Articulatory-Based Audiovisual Speech Synthesis.- Kurdish Spoken Dialect Recognition Using X-vector Speaker Embedding.- An ASR-based Tutor for Learning to Read: How to Optimize Feedback to First Graders.- Velocity Differences Between Velum Raising and Lowering Movements.- Pragmatic Markers of Russian Everyday Speech: Invariants in Dialogue and Monologue.- Language Adaptation for Speaker Recognition Systems using Contrastive Learning.- Evaluating X-vector-based Speaker Anonymization Under White-box Assessment.- Improved Prosodic Clustering for Multispeaker and Speaker-Independent Phoneme-Level Prosody Control.- Initial Experiments on Question Answering from the Intrinsic Structure of Oral History Archives.- Imagined, Intended, and Spoken Speech Envelope Synthesis from Neuromagnetic Signals.- What Causes Phonetic Reduction in Russian Speech: New Evidence from Machine Learning Algorithms.- Toxic Comment Classification Service in Social Network.- Deep Learning based Engagement Recognition in Highly Imbalanced Data.- Intraspeaker Variability of a Professional Lecturer: Ageing, Genre, Pragmatics vs. Voice Acting (Case Study).- An Ensemble Approach for the Diagnosis of COVID-19 from Speech and Cough Sounds.- Where are We in Semantic Concept Extraction for Spoken Language Understanding?.- Learning Mizo Tones from F0 Contours using 1D-CNN.- OCR Improvements for Images of Multi-Page Historical Documents.- X-Bridge: Image-to-Image Translation with Reconstruction Capabilities.- Who is Selling to Whom - Feature Evaluation for Multi-block Classification in Invoice Information Extraction.- Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos in
• Machine Learning.- Text and Synthetic Data for Domain Adaptation in End-to-End Speech Recognition.- Speaker-invariant Speech-To-Intent Classification for Low-Resource Languages.- Speaker-Dependent Visual Command Recognition in Vehicle Cabin: Methodology and Evaluation.- Optimised Code-Switched Language Model Data Augmentation in Four Under-Resourced South African Languages.- Synthesis Speech based Data Augmentation for Low Resource Children ASR.- End-to-End Russian Speech Recognition Models with Multi-Head Attention.- Word-level Style Control for Expressive, Non-attentive Speech Synthesis.- Perceiving Speech Aggression with and without Textual Context on Twitter Social Network Site.-
• Assessing Speaker Interpolation in Neural Text-to-Speech.- A Mobile Application for Detection of Amyotrophic Lateral Sclerosis via Voice Analysis.- Child's Emotional Speech Classification by Human across Two Languages: Russian & Tamil.- Analysis of Dialogues of Typically Developing Children, Children with
• Down Syndrome and ASD using Machine Learning Methods.- Speaker Adaptation with Continuous Vocoder-based DNN-TTS.- Automatic Recognition of the Psychoneurological State of Children:
• Autism Spectrum Disorders, Down Syndrome, Typical Development.- Study on Acoustic Model Personalization in a Context of Collaborative Learning Constrained by Privacy Preservation.- USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments.- A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English.- Dialog Speech Sentiment Classification for Imbalanced Datasets.- Explicit Control of the Level of Expressiveness in DNN-based Speech Synthesis by Embedding Interpolation.- Experimental Analysis of Expert and Quantitative Estimates of Syllable Recordings in the Process of Speech Rehabilitation.- Methods for Using Class Based N-gram Language Models in the Kaldi Toolkit.- Spectral Root Features for Replay Spoof Detection in Voice Assistants.-
• Influence of the Aggressive Internet Environment on Cognitive Personality Disorders (in Relation to the Russian Young Generation of Users).- Media Content vs Nature Stimuli Influence on Human Brain Activity.-
• Can Your Eyes Tell Us Why You Hesitate? Comparing Reading Aloud in Russian as L1 and Japanese as L2.- Recognition of Heavily Accented and Emotional Speech of English and Czech Holocaust Survivors using Various DNN Architectures.- Assessing Speaker-Independent Character Information for Acted Voices.- Influence of Speaker Pre-training on Character Voice Representation.- Opinion Classification via Word and Emoji Embedding Models with LSTM.- An Equal Data Setting for Attention-based Encoder-Decoder and
• HMM/DNN Models: a Case Study in Finnish ASR.- Speaker-aware Training of Speech Emotion Classifier with Speaker Recognition.- Neural Network Recognition of Russian Noun and Adjective Cases in the Google Books Ngram Corpus.- Is it a Filler or a Pause? A Quantitative Analysis of Filled Pauses in
• Hebrew.- Modified Group Delay Function using Different Spectral Smoothing Techniques for Voice Liveness Detection.- Complex Rhythm Adjustments in Multilingual Code-Switching across
• Mandarin, English and Russian.- Increasing the Precision of Dysarthric Speech Intelligibility and Severity
• Level Estimate.- Articulation During Voice Disguise: a Pilot Study.- Improvement of Speaker Number Estimation by Applying an Overlapped Speech Detector.- Mind Your Tweet: Abusive Tweet Detection.- Speaker Authorization for Air Traffic Control Security.- Prosodic Changes with Age: a Longitudinal Study on a Famous European Portuguese Native Speaker.- Automatic Selection of the Most Characterizing Features for Detecting COPD in Speech.- Multilingual Training Set Selection for ASR in Under-Resourced Malian
• Languages.- Human and Transformer-Based Prosodic Phrasing in Two Speech Genres.- Learning Efficient Representations for Keyword Spotting with Triplet Loss.- Regularized Forward-Backward Decoder for Attention Models.- Induced Local Attention for Transformer Models in Speech Recognition.- Applying EEND Diarization to Telephone Recordings from a Call Center.- Acoustic Characteristics of Speech Entrainment in Dialogues in Similar Phonetic Sequences.- Predicting Biometric Error Behaviour from Speaker Embeddings and a Fast Score Normalization Scheme.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 23rd International Conference on Speech and Computer, SPECOM 2021, held in St. Petersburg, Russia, in September 2021.* The 74 papers presented were carefully reviewed and selected from 163 submissions. The papers present current research in the area of computer speech processing including audio signal processing, automatic speech recognition, speaker recognition, computational paralinguistics, speech synthesis, sign language and multimodal processing, and speech and language resources. *Due to the COVID-19 pandemic, SPECOM 2021 was held as a hybrid event.
(source: Nielsen Book Data)

• Intro; SPECOM 2019 Preface; Organization; Contents; The Representation of Speech and Its Processing in the Human Brain and Deep Neural Networks; Abstract; 1 Introduction; 2 Speech Representations; 3 Adaptation to Non-standard Speech; 3.1 Does a DNN Show Human-Like Adaptation to Nonstandard Speech?; 3.2 Are Nonstandard Sounds Processed Similarly in Human Listeners and DNNs?; 4 Concluding Remarks; Acknowledgments; References; A Detailed Analysis and Improvement of Feature-Based Named Entity Recognition for Turkish; 1 Introduction; 2 Previous Work; 3 Datasets; 4 Methodology; 4.1 Model

• 4.2 Features4.3 Evaluation Metrics; 5 Results and Discussion; 6 Conclusion and Future Work; References; A Comparative Study of Classical and Deep Classifiers for Textual Addressee Detection in Human-Human-Machine Conversations; 1 Introduction; 2 Related Work; 3 Classifiers; 3.1 Classical Models; 3.2 Deep Models; 3.3 Fusion; 4 Corpora; 5 Experimental Results; 6 Conclusions; References; Acoustic Event Mixing to Multichannel AMI Data for Distant Speech Recognition and Acoustic Event Classification Benchmarking; 1 Introduction; 2 Preliminary Information; 2.1 Revision of Available Corpora

• 2.2 Problem Formulation3 Proposed Approach to Acoustic Event Mixing; 3.1 Acoustic Event Spatial Reverberation; 3.2 Acoustic Event Mixing; 3.3 Parameter Specification for the AMI Corpus; 4 Evaluation and Results; 4.1 Evaluation Methods and Metrics; 4.2 Evaluation Results; 5 Conclusion; References; Speech-Based L2 Call System for English Foreign Speakers; 1 Introduction; 2 Dataset Description; 3 Features Prediction; 3.1 Features Produced by Universal Sentence Encoder; 3.2 Features Using Python English Grammar Checker Toolkit; 3.3 Part-of-Speech (POS) Features

• 3.4 Response Embedding to Binary Features4 Evaluation; 4.1 Evaluation Metric; 4.2 Experiments and Results; 4.3 Fusion of Multiple Models; 4.4 Comparison and Discussion; 5 Conclusion and Future Work; References; A Pattern Mining Approach in Feature Extraction for Emotion Recognition from Speech; 1 Introduction; 2 Our Approach; 2.1 Dimensionality Reduction; 2.2 Discretization; 2.3 Pattern Mining; 2.4 Feature Extraction; 2.5 Classification; 3 Dataset and Experimental Results; 3.1 Dataset; 3.2 Experimental Results; 4 Conclusion; References

• Towards a Dialect Classification in German Speech Samples1 Introduction; 2 Methods; 2.1 Speech Data; 2.2 Models; 3 Baseline Experiments; 4 Results and Discussion; 5 Conclusions; References; Classification of Regional Accent Using Speech Rhythm Metrics; Abstract; 1 Introduction; 2 MSA Language; 3 Rhythm Modeling; 4 Participants; 5 Experiments and Results; 5.1 Rhythm Metrics Dataset; 5.2 Classifier Design; 5.3 Experiments; 6 Conclusion; References; PocketEAR: An Assistive Sound Classification System for Hearing-Impaired; 1 Introduction; 1.1 Motivation; 2 System Architecture

This book constitutes the proceedings of the 21st International Conference on Speech and Computer, SPECOM 2019, held in Istanbul, Turkey, in August 2019. The 57 papers presented were carefully reviewed and selected from 86 submissions. The papers present current research in the area of computer speech processing including audio signal processing, automatic speech recognition, speaker recognition, computational paralinguistics, speech synthesis, sign language and multimodal processing, and speech and language resources.

• Invited Papers.- The Future of Text-Meaning in Computational Linguistics.- Deep Lexical Semantics.- Toward the Ultimate ASR Language Model.- Practical Prosody.- Text.- Sentiment Detection Using Lexically-Based Classifiers.- First Steps in Building a Verb Valency Lexicon for Romanian.- Hyponymy Patterns.- Temporal Issues and Recognition Errors on the Capitalization of Speech Transcriptions.- Web-Based Lemmatisation of Named Entities.- Combining Multiple Resources to Build Reliable Wordnets.- Active Tags for Semantic Analysis.- Spoken Requests for Tourist Information.- Affisix: Tool for Prefix Recognition.- Variants and Homographs.- An Empirical Bayesian Method for Detecting Out of Context Words.- Semantic Classes in Czech Valency Lexicon.- A Comparison of Language Models for Dialog Act Segmentation of Meeting Transcripts.- New Methods for Pruning and Ordering of Syntax Parsing Trees.- Web People Search with Domain Ranking.- Disyllabic Chinese Word Extraction Based on Character Thesaurus and Semantic Constraints in Word-Formation.- Statistical Word Sense Disambiguation in Contexts for Russian Nouns Denoting Physical Objects.- Where Do Parsing Errors Come From.- Dealing with Small, Noisy and Imbalanced Data.- Statistical Properties of Overlapping Ambiguities in Chinese Word Segmentation and a Strategy for Their Disambiguation.- The Verb Argument Browser.- Thinking in Objects.- Improving Unsupervised WSD with a Dynamic Thesaurus.- Normalization of Temporal Information in Estonian.- Word Sense Disambiguation with Semantic Networks.- A Pattern-Based Framework for Uncertainty Representation in Ontologies.- MSD Recombination for Statistical Machine Translation into Highly-Inflected Languages.- A Computational Framework to Integrate Different Semantic Resources.- Speech.- Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression.- Language Acquisition: The Emergence of Words from Multimodal Input.- A Tagged Corpus-Based Study for Repeats and Self-repairs Detection in French Transcribed Speech.- Study on Speaker Adaptation Methods in the Broadcast News Transcription Task.- Spoken Document Retrieval Based on Approximated Sequence Alignment.- Pitch Accents, Boundary Tones and Contours: Automatic Learning of Czech Intonation.- On the Use of MLP Features for Broadcast News Transcription.- Acoustic Modeling for Speech Recognition in Telephone Based Dialog System Using Limited Audio Resources.- Performance Evaluation for Voice Conversion Systems.- Influence of Reading Errors on the Text-Based Automatic Evaluation of Pathologic Voices.- Czech Pitch Contour Modeling Using Linear Prediction.- Energy Normalization in Automatic Speech Recognition.- HMM-Based Speech Synthesis for the Greek Language.- Advances in Acoustic Modeling for the Recognition of Czech.- Talking Head as Life Blog.- The Module of Morphological and Syntactic Analysis SMART.- An Extension to the Sammon Mapping for the Robust Visualization of Speaker Dependencies.- Analysis of Hypernasal Speech in Children with Cleft Lip and Palate.- Syllable Based Language Model for Large Vocabulary Continuous Speech Recognition of Polish.- Accent and Channel Adaptation for Use in a Telephone-Based Spoken Dialog System.- Efficient Unit-Selection in Text-to-Speech Synthesis.- Prosodic Events Recognition in Evaluation of Speech-Synthesis System Performance.- UU Database: A Spoken Dialogue Corpus for Studies on Paralinguistic Information in Expressive Conversation.- Perceptually Motivated Sub-band Decomposition for FDLP Audio Coding.- Forced-Alignment and Edit-Distance Scoring for Vocabulary Tutoring Applications.- Exploiting Contextual Information for Speech/Non-Speech Detection.- Identification of Speakers from Their Hum.- Reverse Correlation for Analyzing MLP Posterior Features in ASR.- Acquisition of Telephone Data from Radio Broadcasts with Applications to Language Recognition.- Multilingual Weighted Codebooks for Non-native Speech Recognition.- Prosodic Phrases and Semantic Accents in Speech Corpus for Czech TTS Synthesis.- Making Speech Technologies Available in (Serviko) Romani Language.- Emulating Temporal Receptive Fields of Higher Level Auditory Neurons for ASR.- A Framework for Language-Independent Analysis and Prosodic Feature Annotation of Text Corpora.- Quantification of Segmentation and F0 Errors and Their Effect on Emotion Recognition.- Deep Syntactic Analysis and Rule Based Accentuation in Text-to-Speech Synthesis.- Error Prediction-Based Semi-automatic Segmentation of Speech Databases.- Dialogue.- Cognitive and Emotional Interaction.- Architecture Model and Tools for Perceptual Dialog Systems.- The Generation of Emotional Expressions for a Text-Based Dialogue Agent.- Intelligent Voice Navigation of Spreadsheets.- A Semi-automatic Wizard of Oz Technique for Let'sFly Spoken Dialogue System.- Developing a Dialogue System.- Dialogue-Based Processing of Graphics and Graphical Ontologies.- An Issue-Based Approach to Information Search Modelling: Analysis of a Human Dialog Corpus.- Two-Level Fusion to Improve Emotion Classification in Spoken Dialogue Systems.- Automatic Semantic Annotation of Polish Dialogue Corpus.- Evaluation of the Slovak Spoken Dialogue System Based on ITU-T.- Shedding Light on a Troublesome Issue in NLIDBS.- Dialogue Based Text Editing.- SSML for Arabic Language.
• (source: Nielsen Book Data)

The annual Text, Speech and Dialogue Conference (TSD), which originated in 1998, is now starting its second decade. So far almost 900 authors from 45 countries have contributed to the proceedings. TSD constitutes a recognizedplatform for the presen- tion and discussion of state-of-the-art technology and recent achievements in the ?eld of natural language processing. It has become an interdisciplinary forum, interweaving the themes of speech technology and language processing. The conference attracts - searchers not only from Central and Eastern Europe, but also from other parts of the world. Indeed, one of its goals has always been to bring together NLP researchers with different interests from different parts of the world and to promote their mutual co- eration. One of the ambitions of the conference is, as its title says, not only to deal with dialogue systems as such, but also to contribute to improving dialogue between researchers in the two areas of NLP, i. e. , between text and speech people. In our view, the TSD conference was successful in this respect in 2008 as well. This volume contains the proceedings of the 11th TSD conference, held in Brno, Czech Republic in September 2008. Following the review process, 79 papers were - ceptedoutof173submitted, anacceptancerateof45. 7%.
(source: Nielsen Book Data)

• Invited Paper.- An Alternative Approach to Tagging.- Natural Language for Database Query Processing.- An Efficient Denotational Semantics for Natural Language Database Queries.- Email Management.- An Approach to Hierarchical Email Categorization Based on ME.- Developing Methods and Heuristics with Low Time Complexities for Filtering Spam Messages.- Semantic Annotation.- Exploit Semantic Information for Category Annotation Recommendation in Wikipedia.- A Lightweight Approach to Semantic Annotation of Research Papers.- Text Clustering.- A New Text Clustering Method Using Hidden Markov Model.- Identifying Event Sequences Using Hidden Markov Model.- The Dictionary-Based Quantified Conceptual Relations for Hard and Soft Chinese Text Clustering.- On-Line Single-Pass Clustering Based on Diffusion Maps.- Selecting Labels for News Document Clusters.- Ontology Engineering.- Generating Ontologies Via Language Components and Ontology Reuse.- Experiences Using the ResearchCyc Upper Level Ontology.- From OWL Class and Property Labels to Human Understandable Natural Language.- Ontological Text Mining of Software Documents.- Natural Language for Information System Design.- Treatment of Passive Voice and Conjunctions in Use Case Documents.- Natural Language Processing and the Conceptual Model Self-organizing Map.- Automatic Issue Extraction from a Focused Dialogue.- Information Retrieval Systems.- Character N-Grams Translation in Cross-Language Information Retrieval.- Cross-Lingual Information Retrieval by Feature Vectors.- Incomplete and Fuzzy Conceptual Graphs to Automatically Index Medical Reports.- Combining Vector Space Model and Multi Word Term Extraction for Semantic Query Expansion.- The Bootstrapping Based Recognition of Conceptual Relationship for Text Retrieval.- A Framework of NLP Based Information Tracking and Related Knowledge Organizing with Topic Maps.- Natural Language Processing Techniques.- DLSITE
• -1: Lexical Analysis for Solving Textual Entailment Recognition.- Text Segmentation Based on Document Understanding for Information Retrieval.- Named Entity Recognition for Arabic Using Syntactic Grammars.- Four Methods for Supervised Word Sense Disambiguation.- Enhancing Relation Extraction by Eliciting Selectional Constraint Features from Wikipedia.- A Computer Science Electronic Dictionary for NOOJ.- Applying Wikipedia's Multilingual Knowledge to Cross-Lingual Question Answering.- Zero Anaphora Resolution in Chinese and Its Application in Chinese-English Machine Translation.- Short Papers.- Rule-Based Partial MT Using Enhanced Finite-State Grammars in NooJ.- Biomedical Named Entity Recognition: A Poor Knowledge HMM-Based Approach.- Unsupervised Language Independent Genetic Algorithm Approach to Trivial Dialogue Phrase Generation and Evaluation.- Large-Scale Knowledge Acquisition from Botanical Texts.- Lexical-Based Alignment for Reconstruction of Structure in Parallel Texts.- Electronic Dictionaries and Transducers for Automatic Processing of the Albanian Language.- Two Methods of Evaluation of Semantic Similarity of Nouns Based on Their Modifier Sets.- A Service Oriented Architecture for Adaptable Terminology Acquisition.- Domain Relevance on Term Weighting.- Flexible and Customizable NL Representation of Requirements for ETL processes.
• (source: Nielsen Book Data)

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. It covers 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.
(source: Nielsen Book Data)

• Invited Talks.- Language Modeling with Linguistic Cluster Constraints.- Some of Our Best Friends Are Statisticians.- Some Special Problems of Speech Communication.- Recent Advances in Spoken Language Understanding.- Text.- Transformation-Based Tectogrammatical Dependency Analysis of English.- Multilingual Name Disambiguation with Semantic Information.- Inducing Classes of Terms from Text.- Accurate Unlexicalized Parsing for Modern Hebrew.- Disambiguation of the Neuter Pronoun and Its Effect on Pronominal Coreference Resolution.- Constructing a Large Scale Text Corpus Based on the Grid and Trustworthiness.- Disambiguating Hypernym Relations for Roget's Thesaurus.- Dependency and Phrasal Parsers of the Czech Language: A Comparison.- Automatic Word Clustering in Russian Texts.- Feature Engineering in Maximum Spanning Tree Dependency Parser.- Automatic Selection of Heterogeneous Syntactic Features in Semantic Similarity of Polish Nouns.- Bilingual News Clustering Using Named Entities and Fuzzy Similarity.- Extractive Summarization of Broadcast News: Comparing Strategies for European Portuguese.- On the Evaluation of Korean WordNet.- An Adaptive Keyboard with Personalized Language-Based Features.- An All-Path Parsing Algorithm for Constraint-Based Dependency Grammars of CF-Power.- Word Distribution Based Methods for Minimizing Segment Overlaps.- On the Relative Hardness of Clustering Corpora.- Indexing and Retrieval Scheme for Content-Based Multimedia Applications.- Automatic Diacritic Restoration for Resource-Scarce Languages.- Lexical and Perceptual Grounding of a Sound Ontology.- Named Entities in Czech: Annotating Data and Developing NE Tagger.- Identifying Expressions of Emotion in Text.- ECAF: Authoring Language for Embodied Conversational Agents.- Speech.- Dynamic Adaptation of Language Models in Speech Driven Information Retrieval.- Whitening-Based Feature Space Transformations in a Speech Impediment Therapy System.- Spanish-Basque Parallel Corpus Structure: Linguistic Annotations and Translation Units.- An Automatic Version of the Post-Laryngectomy Telephone Test.- Speaker Normalization Via Springy Discriminant Analysis and Pitch Estimation.- A Study on Speech with Manifest Emotions.- Speech Recognition Supported by Prosodic Information for Fixed Stress Languages.- TRAP-Based Techniques for Recognition of Noisy Speech.- Intelligibility Is More Than a Single Word: Quantification of Speech Intelligibility by ASR and Prosody.- Appositions Versus Double Subject Sentences - What Information the Speech Analysis Brings to a Grammar Debate.- Automatic Evaluation of Pathologic Speech - from Research to Routine Clinical Use.- The LIA Speech Recognition System: From 10xRT to 1xRT.- Logic-Based Rhetorical Structuring for Natural Language Generation in Human-Computer Dialogue.- Text-Independent Speaker Identification Using Temporal Patterns.- Recording and Annotation of Speech Corpus for Czech Unit Selection Speech Synthesis.- Sk-ToBI Scheme for Phonological Prosody Annotation in Slovak.- Towards Automatic Transcription of Large Spoken Archives in Agglutinating Languages - Hungarian ASR for the MALACH Project.- Non-uniform Speech/Audio Coding Exploiting Predictability of Temporal Evolution of Spectral Envelopes.- Filled Pauses in Speech Synthesis: Towards Conversational Speech.- Exploratory Analysis of Word Use and Sentence Length in the Spoken Dutch Corpus.- Design of Tandem Architecture Using Segmental Trend Features.- An Automatic Retraining Method for Speaker Independent Hidden Markov Models.- User Modeling to Support the Development of an Auditory Help System.- Fast Discriminant Training of Semi-continuous HMM.- Speech/Music Discrimination Using Mel-Cepstrum Modulation Energy.- Parameterization of the Input in Training the HVS Semantic Parser.- A Comparison Using Different Speech Parameters in the Automatic Emotion Recognition Using Feature Subset Selection Based on Evolutionary Algorithms.- Benefit of Maximum Likelihood Linear Transform (MLLT) Used at Different Levels of Covariance Matrices Clustering in ASR Systems.- Information Retrieval Test Collection for Searching Spontaneous Czech Speech.- Inter-speaker Synchronization in Audiovisual Database for Lip-Readable Speech to Animation Conversion.- Constructing Empirical Models for Automatic Dialog Parameterization.- The Effect of Lexicon Composition in Pronunciation by Analogy.- Festival-si: A Sinhala Text-to-Speech System.- Voice Conversion Based on Probabilistic Parameter Transformation and Extended Inter-speaker Residual Prediction.- Automatic Czech - Sign Speech Translation.- Maximum Likelihood and Maximum Mutual Information Training in Gender and Age Recognition System.- Pitch Marks at Peaks or Valleys?.- Quality Deterioration Factors in Unit Selection Speech Synthesis.- Topic-Focus Articulation Algorithm on the Syntax-Prosody Interface of Romanian.- Translation and Conversion for Czech Sign Speech Synthesis.- Dialog.- A Wizard-of-Oz System Evaluation Study.- New Measures for Open-Domain Question Answering Evaluation Within a Time Constraint.- A Methodology for Domain Dialogue Engineering with the Midiki Dialogue Manager.- The Intonational Realization of Requests in Polish Task-Oriented Dialogues.- Analysis of Changes in Dialogue Rhythm Due to Dialogue Acts in Task-Oriented Dialogues.- Recognition and Understanding Simulation for a Spoken Dialog Corpus Acquisition.- First Approach in the Development of Multimedia Information Retrieval Resources for the Basque Context.- The Weakest Link.- A Spoken Dialog System for Chat-Like Conversations Considering Response Timing.- Digitisation and Automatic Alignment of the DIALOG Corpus: A Prosodically Annotated Corpus of Czech Television Debates.- Setting Layout in Dialogue Generating Web Pages.- Graph-Based Answer Fusion in Multilingual Question Answering.- Using Query-Relevant Documents Pairs for Cross-Lingual Information Retrieval.- Detection of Dialogue Acts Using Perplexity-Based Word Clustering.- Dialogue Management for Intelligent TV Based on Statistical Learning Method.- Multiple-Taxonomy Question Classification for Category Search on Faceted Information.- Indexing and Retrieval Scheme for Content-Based Multimedia Applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 10th International Conference on Text, Speech and Dialogue, TSD 2007, held in Pilsen, Czech Republic, in September 2007. The 80 revised full papers presented in this volume cover a wealth of state-of-the-art research results in the field of natural language processing with an emphasis on text, speech, and spoken dialogue ranging from theoretical and methodological issues to applications in various fields.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Invited Lectures; Regular Papers; Short Papers; Subject Index; Author Index

Contains papers that cover a range of Natural Language Processing (NLP) applications, including machine learning and translation, logic, computational phonology, morphology and semantics, data mining, information extraction and disambiguation, as well as programming, optimization and compression of finite-state networks.

• Table of Contents Introduction to Chatbots and the Rasa Framework Natural Language Understanding in Rasa Rasa Core Handling Business Logic Working with Response Selector to Handle chitchat and FAQs Knowledge Base Actions to Handle Question Answering Entity Roles and Groups for Complex Named Entity Recognition Customization of Rasa Testing and Production Deployment Conversation-Driven Development and Interactive Learning Debugging, Optimization, and the Community Ecosystem.
• (source: Nielsen Book Data)

Create next-level AI assistants and transform how customers communicate with businesses with the power of natural language understanding and dialogue management using Rasa Key Features Understand the architecture and put the underlying principles of the Rasa framework to practice Learn how to quickly build different types of chatbots such as task-oriented, FAQ-like, and knowledge graph-based chatbots Explore best practices for working with Rasa and its debugging and optimizing aspects Book DescriptionThe Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work - Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle. What you will learn Use the response selector to handle chitchat and FAQs Create custom actions using the Rasa SDK Train Rasa to handle complex named entity recognition Become skilled at building custom components in the Rasa framework Validate and test dialogs end to end in Rasa Develop and refine a chatbot system by using conversation-driven deployment processing Use TensorBoard for tuning to find the best configuration options Debug and optimize dialogue systems based on Rasa Who this book is forThis book is for NLP professionals as well as machine learning and deep learning practitioners who have knowledge of natural language processing and want to build chatbots with Rasa. Anyone with beginner-level knowledge of NLP and deep learning will be able to get the most out of the book.
(source: Nielsen Book Data)

Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technologyNatural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you'll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you'll use in all different kinds of NLP programs. By the time you're done, you'll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what's inside Design, develop, and deploy basic NLP applications NLP libraries such as AllenNLP and Fairseq Advanced NLP concepts such as attention and transfer learning about the readerAimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.
(source: Nielsen Book Data)

• Chapter 1. Introduction
• Chapter 2. Literature Review
• Chapter 3. Data Analysis
• Chapter 4. SynTime: Token Types and Heuristic Rules
• 5. TOMN: Constituent-based Tagging Scheme
• Chapter 6. UGTO: Uncommon Words and Proper Nouns
• Chapter 7. Conclusion and Future Work.

This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use.

• 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
• Appendix A Kaggle primer
• Appendix B Introduction to fundamental deep learning tools.

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.