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• Chapter 1: Introduction to Language Models
• Chapter 2: Introduction to Transformers
• Chapter 3: BERT
• Chapter 4: Hugging Face
• Chapter 5: Tasks Using the Huggingface Library
• Chapter 6: Fine-Tuning Pre-Trained Models
• Appendix A: Vision Transformers.

Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing. This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation. After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library.

• PrefaceAffective Computing and Sentiment AnalysisMany Facets of Sentiment AnalysisReflections on Sentiment/Opinion AnalysisChallenges in Sentiment AnalysisSentiment Resources: Lexicons and DatasetsGenerative Models for Sentiment Analysis and Opinion MiningSocial Media Summarization.- Deception Detection and Opinion SpamConcept-Level Sentiment Analysis with SenticNetIndex.
• (source: Nielsen Book Data)

Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers' sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.
(source: Nielsen Book Data)

• 1. Memory-based learning in natural language processing
• 2. Inspirations from linguistics and artificial intelligence
• 3. Memory and similarity
• 4. Application to morpho-phonology
• 5. Application to shallow parsing
• 6. Abstraction and generalization
• 7. Extensions.
• (source: Nielsen Book Data)

Memory-based language processing - a machine learning and problem solving method for language technology - is based on the idea that the direct reuse of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information. By applying the model to a range of benchmark problems, the authors show that for linguistic areas ranging from phonology to semantics, it produces excellent results. They also describe TiMBL, a software package for memory-based language processing. The first comprehensive overview of the approach, this book will be invaluable for computational linguists, psycholinguists and language engineers.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third International Conference PorTAL 2002 - Portugal for Natural Language Processing, held in Faro, Portugal, in June 2002. The 23 reviewed regular papers and 11 short papers presented were carefully reviewed and selected from 48 submissions. The papers are organized in topical sections on pragmatics, discourse, semantics, and the lexicon; interpreting and generating spoken and written language; language-oriented information retrieval, question answering, summarization, and information extraction; language-oriented machine learning; multi-lingual processing, machine translation, and translation aids; natural language interfaces and dialogue systems; tools and resources; and evaluation of systems.
(source: Nielsen Book Data)

• Computing with words - introduction, implications and applications
• from computing with numbers to computing with words from manipulation of measurements to manipulation of perceptions
• computing with words - the problems and solutions. Issues of computing with words at large: models and mechanisms of thinking - a multigranular approach
• granular relational computing with semiotic descriptors using BK-products of fuzzy relations
• computing granular word meanings - a fuzzy linguistic approach in computational semiotics
• extracting information from text
• computing with word and cognitive science. Linguistic uncertainty modelling: combining linguistic information in a distributed intelligent agent model for information gathering on the Internet
• a behavioural model for linguistic uncertainty. Knowledge acquistion: applying modifiers to knowledge acquisition
• computing with words and machine learning in medical diagnostics
• numeric robustness and linguistic perspicuity - a hybrid approach to knowledge acquisition
• computing with descriptive and veristic words - knowledge representation and reasoning
• principles of words based knowledge representation and knowledge processing for CWW. Reasoning and querying: computing with words in intelligent database querying: standalone and Internet-based applications
• fuzzy qualitative reasoning with words
• control with words - the modular approach
• prospects for integrating analysis of variance with soft computing. Verb systems: beyond process- and action-symbols
• let machines understand computational process- and action-symbols.
• (source: Nielsen Book Data)

Fuzzy logic refers to a computer's ability to make decisions involving "grey" or "fuzzy" areas. As linguistics contains numerous "grey" areas, computing with words through the use of fuzzy logic is an extremely hot topic in database and Internet research. This book explores the state of the art in linguistic computation, discussing how current research findings are extending the application of fuzzy logic beyond control engineering and intelligent systems into the use of language on a computer. Fuzzy logic pioneer, Dr. Lofti Zadeh, provides the introduction for this thought-provoking work.
(source: Nielsen Book Data)

• (NOTE: Each chapter ends with Historical Perspective and Further Reading.)
• 1. Introduction. Motivations. Spoken Language System Architecture. Book Organization. Target Audiences. I. FUNDAMENTAL THEORY.
• 2. Spoken language Structure. Sound and Human Speech Systems. Phonetics and Phonology. Syllables and Words. Syntax and Semantics.
• 3. Probability, Statistics, and Information Theory. Probability Theory. Estimation Theory. Significance Testing. Information Theory.
• 4. Pattern Recognition. Bayes' Decision Theory. How to Construct Classifiers. Discriminative Training. Unsupervised Estimation Methods. Classification and Regression Trees. II. SPEECH PROCESSING.
• 5. Digital Signal Processing. Digital Signals and Systems. Continuous-Frequency Transforms. Discrete-Frequency Transforms. Digital Filters and Windows. Digital Processing of Analog Signals. Multirate Signal Processing. Filterbanks. Stochastic Processes.
• 6. Speech Signal Representations. Short-Time Fourier Analysis. Acoustical Model of Speech Production. Linear Predictive Coding. Cepstral Processing. Perceptually Motivated Representations. Formant Frequencies. The Role of Pitch.
• 7. Speech Coding. Speech Coders Attributes. Scalar Waveform Coders. Scalar Frequency Domain Coders. Code Excited Linear Prediction (CELP). Low-Brit Speech Coders. III. SPEECH RECOGNITION.
• 8. Hidden Markov Models. The Markov Chain. Definition of the Hidden Markov Model. Continuous and Semicontinuous HMMs. Practical Issues in Using HMMs. HMM Limitations.
• 9. Acoustic Modeling. Variability in the Speech Signal. How to Measure Speech Recognition Errors. Signal Processing-Extracting Features. Phonectic Modeling-Selecting Appropriate Units. Acoustic Modeling-Scoring Acoustic Features. Adaptive Techniques-Minimizing Mismatches. Confidence Measures: Measuring the Reliability. Other Techniques. Case Study: Whisper.
• 10. Environmental Robustness. The Acoustical Environment. Acoustical Transducers. Adaptive Echo Cancellation (AEC). Multimicrophone Speech Enhancement. Environment Compensation Preprocessing. Environment Model Adaptation. Modeling Nonstationary Noise.
• 11. Language Modeling. Formal Language Theory. Stochastic Language Models. Complexity Measure of Language Models. N-Gram Smoothing. Adaptive Language Models. Practical Issues.
• 12. Basic Search Algorithms. Basic Search Algorithms. Search Algorithms for Speech Recognition. Language Model States. Time-Synchronous Viterbi Beam Search. Stack Decoding (A Search).
• 13. Large-Vocabulary Search Algorithms. Efficient Manipulation of a Tree Lexicon. Other Efficient Search Techniques. N-Best and Multipass Search Strategies. Search-Algorithm Evaluation. Case Study-Microsoft Whisper. IV. TEXT-TO-SPEECH SYSTEMS.
• 14. Text and Phonetic Analysis. Modules and Data Flow. Lexicon. Document Structured Detection. Text Normalization. Linguistic Analysis. Homograph Disambiguation. Morphological Analysis. Letter-to-Sound Conversion. Evaluation. Case Study: Festival.
• 15. Prosody. The Role of Understanding. Prosody Generation Schematic. Speaking Style. Symbolic Prosody. Duration Assignment. Pitch Generation. Prosody Markup Languages. Prosody Evaluation.
• 16. Speech Synthesis. Attributes of Speech Synthesis. Formant Speech Synthesis. Concatenative Speech Synthesis. Prosodic Modification of Speech. Source-Filter Models for Prosody Modification. Evaluation of TTS Systems. V. SPOKEN LANGUAGE SYSTEMS.
• 17. Spoken Language Understanding. Written vs. Spoken Languages. Dialog Structure. Semantic Representation. Sentence Interpretation. Discourse Analysis. Dialog Management. Response Generation and Rendition. Evaluation. Case Study-Dr. Who.
• 18. Applications and User Interfaces. Application Architecture. Typical Applications. Speech Interface Design. Internationalization. Case Study-MIPAD. Index.
• (source: Nielsen Book Data)

This will be the definitive book on spoken language systems written by the people at Microsoft Research who have developed the voic-activated technologies that will be imbedded in Windows 2000 and other key Microsoft products of the future. This is not a Microsoft book, however, this is a book on the science and linguistics of this technology and how to use it in developing and building hardware and software products.
(source: Nielsen Book Data)

• Contributors
• Preface
• 1. Architectures and mechanisms in sentence comprehension Martin J. Pickering, Charles Clifton, Jr., and Matthew W. Crocker
• Part I. Frameworks: 2. Evaluating models of human sentence processing Charles Clifton, Jr.
• 3. Specifying architectures for language processing: process, control, and memory in parsing and interpretation Richard L. Lewis
• 4. Modeling thematic and discourse context effects with a multiple constraints approach: implications for the architecture of the language comprehension system Michael K. Tanenhaus, Michael J. Spivey-Knowlton, and Joy E. Hanna
• 5. Late closure in context: some consequences for parsimony Gerry T. M. Altmann
• Part II. Syntactic and Lexical Mechanisms: 6. The modular statistical hypothesis: exploring lexical category ambiguity Steffan Corley and Matthew W. Crocker
• 7. Lexical syntax and parsing architecture Paola Merlo and Suzanne Stevenson
• 8. Constituency, context, and connectionism in syntactic parsing James Henderson
• Part III. Syntax and Semantics: 9. On the electrophysiology of language comprehension: implications for the human language system Colin Brown and Peter Hagoort
• 10. Parsing and incremental understanding during reading Martin J. Pickering and Matthew J. Traxler
• 11. Syntactic attachment and anaphor resolution: the two sides of relative clause attachment Barbara Hemforth, Lars Konieczny, and Christoph Scheepers
• 12. Cross-linguistic psycholinguistics Marica De Vincenzi
• Part IV. Interpretation: 13. On interpretation: minimal 'lowering' Lyn Frazier
• 14. Focus effects associated with negative quantifiers Linda M. Moxey and Anthony J. Sanford
• 15. Constraints and mechanisms in theories of anaphor processing Amit Almor
• Author index
• Subject index.
• (source: Nielsen Book Data)

The architectures and mechanisms underlying language processing form one important part of the general structure of cognition. This book, written by leading experts in the field, brings together linguistic, psychological and computational perspectives on some of the fundamental issues. Several general introductory chapters offer overviews on important psycholinguistic research frameworks and highlight both shared assumptions and controversial issues. Subsequent chapters explore syntactic and lexical mechanisms; statistical and connectionist models of language understanding; the crucial importance of linguistic representations in explaining behavioural phenomena; evidence from a variety of studies and methodologies concerning the interaction of syntax and semantics; and the implications for cognitive architecture. The book concludes with a set of contributions on select issues of interpretation, including quantification, focus and anaphora in language understanding. Architectures and Mechanisms for Language Processing will appeal to students and scholars alike as a comprehensive and timely survey of recent work in this interdisciplinary area.
(source: Nielsen Book Data)

• Figures
• Preface
• 1. Introduction
• 2. National Language Generation in practice
• 3. The architecture of a Natural Language Generation system
• 4. Document planning
• 5. Microplanning
• 6. Surface realisation
• 7. Beyond text generation
• Appendix
• References
• Index.
• (source: Nielsen Book Data)

This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Second International Conference on Natural Language Processing, NLP 2000, held in Patras, Greece, in June 2000. The 39 revised full papers presented were carefully reviewed and selected from a total of 71 submissions. The book is organized in topical sections on tokenization and morphological analysis, lexical knowledge representation, parsing and discourse analysis, anaphora resolution, machine translation, language generation, man-machine interaction and word sense recognition/disambiguation, and information extraction.
(source: Nielsen Book Data)

• Part 1 Introduction, Emmanual Roche and Yves Schabes: preliminaries
• finite-state automata
• finite-state transducers
• bibliographical notes. Part 2 Finite-stage morphology - inflections and derivations in a single framework using dictionaries and rules, David Clemenceau: towards a structured dictionary
• MORPHO - a morphological analyzer based on a dictionary and a two-level system
• a single framework for inflections and derivations recognition and generation
• conclusion. Part 3 Representations and finite-state components in natural language, Kimmo Koskenniemi: a framework, two-level morphology
• finite-state syntactic grammar
• experiences. Part 4 The replace operator, Lauri Karttunen: unconditional replacement
• conditional replacement
• comparisons
• conclusion. Part 5 Finite-state approximation of phrase-structure grammars, Fernando C.N. Pereira and Rebecca N. Wright: motivation
• the approximation method
• formal properties
• implementation and example
• informal analysis
• related work and conclusions. Part 6 The lexical analysis of natural languages, Max D. Silberztein: the lexical analysis of programming langauges and of natural languages
• the units of analysis
• the representation of simple words
• representation of compound words
• representation of the results of the analysis, elimination of ambiguities by local grammars
• tagging programmes and lexical analysis
• conclusion. Part 7 Deterministic part-of-speech tagging with finite-state transducers, Emmanuel Roche and Yves Schabes: overview of Brill's tagger
• complexity of Brill's tagger
• construction of the finite-state tagger
• lexical tagger
• tagging unknown words
• empirical evaluation
• finite-state transudcers
• determinization
• subsequentiality of transformation-based systems
• implementation of finite-state transducers
• conclusion. Part 8 Parsing with finite-state transducers, Emmanuel Roche: background
• a top-down parser for context-free grammars
• morphology
• a parser for transformation grammars
• finite-state acceleration
• a transducer parser for tree-adjoining grammars
• conclusion. Part 9 Designing a (finite-state) parsing grammar, Atro Voutilainen: framework
• grammatical representation
• sample rule
• heuristic techniques
• final remarks. Part 10 Applying a finite-state intersection grammar, Pasi Tapanainen: straight intersection
• sequential methods
• parallel intersection
• hybrid intersection-search method
• a small comparison
• theoretical worst-case study
• conclusion. Part 11 The construction of local grammars, Maurice Gross: linguistic modules
• transformations
• conclusion. Part 12 On the use of sequential transducers in natural language processing, Mehryar Mohri: definitions
• characterization and extensions
• phonology and morphology
• representation of large dictionaries
• syntax
• speech processing
• conclusion. (Part contents).
• (source: Nielsen Book Data)

Describes the fundamental properties of finite-state devices and illustrates their uses. The topics in the text range from the theoretical to the applied, and include finite-state morphology, approximation of phrase-structure grammars and speech recognition using weighted finite automata.
(source: Nielsen Book Data)

This handbook provides easy access to current practice and requirements in the main spoken language technologies.
(source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

This comprehensive state-of-the-art book is the first devoted to the important and timely issue of evaluating NLP systems. It addresses the whole area of NLP system evaluation, including aims and scope, problems and methodology. The authors provide a wide-ranging and careful analysis of evaluation concepts, reinforced with extensive illustrations; they relate systems to their environments and develop a framework for proper evaluation. The discussion of principles is completed by a detailed review of practice and strategies in the field, covering both systems for specific tasks, like translation, and core language processors. The methodology lessons drawn from the analysis and review are applied in a series of example cases. A comprehensive bibliography, a subject index, and term glossary are included.
(source: Nielsen Book Data)

• Introduction
• transformational grammars for natural language
• two-level grammar to acceptor
• two-level processing systems
• meaning and interpretation
• is English context-free?
• issues and applications.
• (source: Nielsen Book Data)

This book is about linguistic analysis and is based on two complementary issues. The first concerns representation - the grammars that describe and generate the sentences that make up a given language. The second deals with processing and computation, showing why a language's grammar has important implications for the way in which it can be processed. Krulee builds on a theoretical base that begins with a grammar. He then proceeds to derive computational methods systematically based upon that grammar. The text discusses the issue of deterministic processing and includes material on how semantic interpretation can be added, explains algorithms that describe systematic relationships between the grammar for a given language and the ability to process sentences, provides coverage of a two-level grammar and uses it to describe a variety of constructions in English.
(source: Nielsen Book Data)
This exploration of computerized linguistic analysis focuses on the grammars that describe and generate the sentences that make up a given language, and why a language's grammar has important implications for the way in which it can be processed.
(source: Nielsen Book Data)

• Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification
• How Challenging is Multimodal Irony Detection?
• Less is more: A Prototypical Framework for Efficient Few-Shot Named Entity Recognition
• Don⁰́₉t Lose the Message while Paraphrasing: A Study on Content Preserving Style Transfer
• A Review of Parallel Corpora for Automatic Text Simplification. Key Challenges Moving Forward
• Explaining a Deep Learning Model for Aspect-Based Sentiment Classification Using Post-hoc Local Classifiers
• Arabic Privacy Policy Corpus and Classification
• SmartEDU: Accelerating Slide Deck Production with Natural Language Processing
• Explainable Integration of Knowledge Graphs using Large Language Models
• Cross-domain and cross-language irony detection: The impact of bias on models⁰́₉ generalization
• Prompt and Instruction-Based Tuning for Response Generation in Conversational Question Answering
• IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran
• Comparing object recognition models and studying hyperparameter selection for the detection of bolts
• Morphosyntactic Evaluation for Text Summarization in Morphologically Rich Languages: A Case Study for Turkish
• Building Knowledge Graphs in Heliophysics and Astrophysics
• Text to Image Synthesis Using Bridge Generative Adversarial Network and Char CNN Model
• Evaluation of transformer-based models for punctuation and capitalization restoration in Spanish and Portuguese
• Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition
• Could KeyWord Masking strategy improve language model?
• Regularization, Semi-supervision, and Supervision for a Plausible Attention-Based Explanation
• Node-Weighted Centrality Ranking for Unsupervised Long Document Summarization
• Characterization of the city of the future from a science fiction corpus
• On the Rule-based Extraction of Statistics Reported in Scientific Papers
• GRAM: Grammar-Based Refined-Label Representing Mechanism in the Hierarchical Semantic Parsing Task
• Expanding Domain-specific Knowledge Graphs with Unknown Facts
• Knowledge Graph Representation Learning via Generated Descriptions
• LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
• A Comparative Study of Evaluation Metrics for Long-Document Financial Narrative Summarization with Transformers
• Effective Information Retrieval, Question Answering and Abstractive Summarization on Large-scale Biomedical Document Corpora
• Abstractive Summarization Based Question-Answer System for Structural Information
• Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis
• A Few-shot Approach to Resume Information Extraction via Prompts
• Decoding Strategies for Code Conciseness and Efficiency in Transformer-Generated Programs
• SP-BERT: A Language Model for Political Text in Scandinavian Languages
• Improving Context-Awareness on Multi-Turn Dialogue Modeling with Extractive Summarization Techniques
• Document Knowledge Transfer for Aspect-Based Sentiment Classification Using a Left-Center-Right Separated Neural Network with Rotatory Attention
• Argument and counter-argument generation: a critical survey
• Novel Benchmark Data Set for Automatic Error Detection and Correction
• Weakly-Supervised Multimodal Learning for Predicting the Gender of Twitter Users
• Cross-Domain Toxic Spans Detection
• How shall a machine call a thing?
• Detecting Artificially Generated Academic Text: the Importance of Mimicking Human Utilization of Large Language Models
• Leveraging Small-BERT and Bio-BERT for Abbreviation Identification in Scientific Text
• RoBERTweet: A BERT Language Model for Romanian Tweets
• Evaluating the Effect of Letter Case on Named Entity Recognition Performance.

This book constitutes the refereed proceedings of the 28th International Conference on Applications of Natural Language to Information Systems, NLDB 2023, held in Derby, UK, in June 21⁰́₃23, 2023 The 31 full papers and 14 short papers included in this book were carefully reviewed and selected from 89 submissions. They focus on the developments of the application of natural language to databases and information systems in the wider meaning of the term

• Information Systems.- Information Retrieval.- Document Representation.- Information Retrieval query processing.- Retrieval Models and Ranking.- Evaluation of retrieval results.- Specialised information retrieval.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference of the CLEF Association, CLEF 2022, held in Bologna, Italy in September 2022.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 7 full papers presented together with 3 short papers in this volume were carefully reviewed and selected from 14 submissions. This year, the contributions addressed the following challenges: authorship attribution, fake news detection and news tracking, noise-detection in automatically transferred relevance judgments, impact of online education on children's conversational search behavior, analysis of multi-modal social media content, knowledge graphs for sensitivity identification, a fusion of deep learning and logic rules for sentiment analysis, medical concept normalization and domain-specific information extraction. In addition to this, the volume presents 7 "best of the labs" papers which were reviewed as full paper submissions with the same review criteria. 14 lab overview papers were accepted and represent scientific challenges based on new datasets and real world problems in multimodal and multilingual information access.
(source: Nielsen Book Data)