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• Cover
• Copyright
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Customer Feedback
• Table of Contents
• Preface
• Chapter 1: Getting Started with Microsoft Cognitive Services
• Cognitive Services in action for fun and life changing purposes
• Setting up boilerplate code
• Detecting faces with the Face API
• Overview of what we are dealing with
• Vision
• Computer Vision
• Emotion
• Face
• Video
• Speech
• Bing Speech
• Speaker Recognition
• Custom Recognition
• Language
• Bing Spell Check
• Language Understanding Intelligent Service (LUIS)
• Linguistic Analysis
• Text Analysis
• Web Language Model
• Knowledge
• Academic
• Entity Linking
• Knowledge Exploration
• Recommendations
• Search
• Bing Web Search
• Bing Image Search
• Bing Video Search
• Bing News Search
• Bing Autosuggest
• Getting feedback on detected faces
• Summary
• Chapter 2: Analyzing Images to Recognize a Face
• Learning what an image is about using Computer Vision API
• Setting up a chapter example project
• Generic image analysis
• Recognizing celebrities using domain models
• Utilizing Optical Character Recognition
• Generating image thumbnails
• Diving deep into the Face API
• Retrieving more information from the detected faces
• Deciding whether two faces belong to the same person
• Finding similar faces
• Grouping similar faces
• Adding identification to our Smart-House application
• Creating our Smart-House application
• Adding people to be identified
• Identifying a person
• Summary
• Chapter 3: Analyzing Videos
• Knowing your mood using the Emotion API
• Getting images from a web camera
• Letting the smart-house know your mood
• Diving into the Video API
• Video operations as common code
• Getting operation results
• Wiring up execution in the ViewModel.

• Detecting and tracking faces in videos
• Detecting motion
• Stabilizing shaky videos
• Generating video thumbnails
• Analyzing emotions in videos
• Summary
• Chapter 4: Letting Applications Understand Commands
• Creating language-understanding models
• Register an account and get a license key
• Creating an application
• Recognizing key data using entities
• Understanding what the user wants using intents
• Simplifying development using pre-built models
• Pre-built applications
• Training a model
• Training and publishing the model
• Connecting to the smart-house application
• Model improvement through active usage
• Visualizing performance
• Resolving performance problems
• Adding model features
• Adding labeled utterances
• Looking for incorrect utterance labels
• Changing the schema
• Active learning
• Executing operations based on commands
• Maintaining conversations from unclear utterances
• Completing actions from intents
• Action fulfillment
• Summary
• Chapter 5: Speak with Your Application
• Converting text to audio and vice versa
• Speaking to the application
• Letting the application speak back
• Audio output format
• Error codes
• Supported languages
• Utilizing LUIS based on spoken commands
• Knowing who is speaking
• Adding speaker profiles
• Enrolling a profile
• Identifying the speaker
• Verifying a person through speech
• Customizing speech recognition
• Creating a custom acoustic model
• Creating a custom language model
• Deploying the application
• Summary
• Chapter 6: Understanding Text
• Setting up a common core
• New project
• Web requests
• Data contracts
• Correcting spelling errors
• Natural Language Processing using the Web Language Model
• Breaking a word into several
• Generating the next word in a sequence of words.

• Learning if a word is likely to follow a sequence of words
• Learning if certain words is likely to appear together
• Extracting information through textual analysis
• Detecting language
• Extracting key phrases from text
• Learning if a text is positive or negative
• Exploring text using linguistic analysis
• Introduction to linguistic analysis
• Analyzing text from a linguistic viewpoint
• Summary
• Chapter 7: Extending Knowledge Based on Context
• Linking entities based on context
• Providing personalized recommendations
• Creating a model
• Importing catalog data
• Importing usage data
• Building a model
• Consuming recommendations
• Recommending items based on prior activities
• Summary
• Chapter 8: Querying Structured Data in a Natural Way
• Tapping into academic content using the Academic API
• Setting up an example project
• Interpreting natural language queries
• Finding academic entities from query expressions
• Calculating the distribution of attributes from academic entities
• Entity attributes
• Creating the backend using the Knowledge Exploration Service
• Defining attributes
• Adding data
• Building the index
• Understanding natural language
• Local hosting and testing
• Going for scale
• Hooking into Microsoft Azure
• Deploying the service
• Answering FAQs using QnA Maker
• Creating a knowledge base from frequently asked questions
• Training the model
• Publishing the model
• Improving the model
• Summary
• Chapter 9: Adding Specialized Searches
• Searching the Web from the Smart-House application
• Preparing the application for web searches
• Searching the Web
• Getting the news
• News from queries
• News from categories
• Trending news
• Searching for images and videos
• Using a common user interface
• Searching for images
• Searching for videos.

• Helping the user with auto suggestions
• Adding Autosuggest to the user interface
• Suggesting queries
• Search commonalities
• Languages
• Pagination
• Filters
• Safe search
• Freshness
• Errors
• Summary
• Chapter 10: Connecting the Pieces
• Connecting the pieces
• Creating an intent
• Updating the code
• Executing actions from intents
• Searching news on command
• Describing news images
• Real-life applications using Microsoft Cognitive Services
• Uber
• DutchCrafters
• CelebsLike.me
• Pivothead
• wearable glasses
• Zero Keyboard
• The common theme
• Where to go from here
• Summary
• Appendix A: LUIS Entities and Intents
• LUIS pre-built intents
• LUIS pre-built entities
• Appendix B: Additional Information on Linguistic Analysis
• Part-of-Speech Tags
• Phrase types
• Appendix C: License Information
• Video Frame Analyzer
• OpenCvSharp3
• Newtonsoft. Json
• NAudio
• Definitions
• Grant of Rights
• Conditions and Limitations
• Index.

Learn to build interactive and efficient applications by leveraging 24 effective cognitive services APIs powered by Microsoft About This Book * Explore the capabilities of 24 of the APIs released as part of the Cognitive Services platform * Build intelligent apps that combine the power of computer vision, speech recognition, and language processing * Give your apps human-like cognitive intelligence with this hands-on guide Who This Book Is For .NET developers who want to add AI capabilities to their applications will find this book useful. No knowledge of machine learning or AI is necessary to work through this book. What You Will Learn * Identify a person through visual inspection and audio * Reduce user effort by utilizing AI-like capabilities * Understand how to analyze images and text in different ways * Find out how to analyze images using Vision APIs * Add video analysis to applications using Vision APIs * Utilize Search to find anything you want * Analyze text to extract information and explore text structure In Detail Microsoft has revamped its Project Oxford to launch the all new Cognitive Services platform-a set of 30 APIs to add speech, vision, language, and knowledge capabilities to apps. This book will introduce you to 24 of the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see how the power of these APIs can be combined to build real-world apps that have cognitive capabilities. The book is split into three sections: computer vision, speech recognition and language processing, and knowledge and search. You will be taken through the vision APIs at first as this is very visual, and not too complex. The next part revolves around speech and language, which are somewhat connected. The last part is about adding real-world intelligence to apps by connecting them to Knowledge and Search APIs. By the end of this book, you will be in a position to understand what Microsoft Cognitive Service can offer and how to use the different APIs. Style and approach This book takes you through essential API capabilities and shows how to utilize them to suit the needs of your application.
(source: Nielsen Book Data)

• Evaluation and Visualization. The Scholarly Impact of CLEF (2000-2009) / Theodora Tsikrika [and others]
• A Quantitative Look at the CLEF Working Notes / Thomas Wilhelm-Stein and Maximilian Eibl
• Building a Common Framework for IIR Evaluation / Mark Michael Hall and Elaine Toms
• Improving Ranking Evaluation Employing Visual Analytics / Marco Angelini [and others]
• A Proposal for New Evaluation Metrics and Result Visualization Technique for Sentiment Analysis Tasks / Francisco José Valverde-Albacete, Jorge Carrillo-de-Albornoz and Carmen Peláez-Moreno
• A New Corpus for the Evaluation of Arabic Intrinsic Plagiarism Detection / Imene Bensalem, Paolo Rosso and Salim Chikhi
• Selecting Success Criteria: Experiences with an Academic Library Catalogue / Paul Clough and Paula Goodale
• A Dependency-Inspired Semantic Evaluation of Machine Translation Systems / Mohammad Reza Mirsarraf and Nazanin Dehghani
• A Turing Test to Evaluate a Complex Summarization Task / Alejandro Molina, Eric SanJuan and Juan-Manuel Torres-Moreno
• A Formative Evaluation of a Comprehensive Search System for Medical Professionals / Veronika Stefanov [and others].

• Multilinguality and Less-Resourced Languages. Exploiting Multiple Translation Resources for English-Persian Cross Language Information Retrieval / Hosein Azarbonyad, Azadeh Shakery and Heshaam Faili
• ALQASIM: Arabic Language Question Answer Selection in Machines / Ahmed Magdy Ezzeldin, Mohamed Hamed Kholief and Yasser El-Sonbaty
• A Web-Based CLIR System with Cross-Lingual Topical Pseudo Relevance Feedback / Xuwen Wang, Xiaojie Wang and Qiang Zhang
• A Case Study in Decompounding for Bengali Information Retrieval / Debasis Ganguly, Johannes Leveling and Gareth J.F. Jones
• Context-Dependent Semantic Annotation in Cross-Lingual Biomedical Resources / Rafael Berlanga [and others]
• A Comparative Evaluation of Cross-Lingual Text Annotation Techniques / Lei Zhang [and others].

• Applications. Mining Query Logs of USPTO Patent Examiners / Wolfgang Tannebaum and Andreas Rauber
• Relevant Clouds: Leveraging Relevance Feedback to Build Tag Clouds for Image Search / Luis A. Leiva, Mauricio Villegas and Roberto Paredes
• Counting Co-occurrences in Citations to Identify Plagiarised Text Fragments / Solange de L. Pertile, Paolo Rosso and Viviane P. Moreira
• The Impact of Belief Values on the Identification of Patient Cohorts / Travis Goodwin, Sanda M. Harabagiu
• Semantic Discovery of Resources in Cloud-Based PACS/RIS Systems / Rafael Berlanga [and others]
• Subtopic Mining Based on Head-Modifier Relation and Co-occurrence of Intents Using Web Documents / Se-Jong Kim and Jong-Hyeok Lee.

• Lab Overviews. Cultural Heritage in CLEF (CHiC) 2013 / Vivien Petras [and others]
• Overview of the ShARe/CLEF eHealth Evaluation Lab 2013 / Hanna Suominen [and others]
• Overview of CLEF-IP 2013 Lab / Florina Piroi, Mihai Lupu and Allan Hanbury
• ImageCLEF 2013: The Vision, the Data and the Open Challenges / Barbara Caputo [and others]
• Overview of INEX 2013 / Patrice Bellot [and others]
• Recent Trends in Digital Text Forensics and Its Evaluation / Tim Gollub [and others]
• QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation / Anselmo Peñas [and others]
• Multilingual Question Answering over Linked Data (QALD-3): Lab Overview / Philipp Cimiano [and others]
• Overview of RepLab 2013: Evaluating Online Reputation Monitoring Systems / Enrique Amigó [and others]
• Entity Recognition in Parallel Multi-lingual Biomedical Corpora: The CLEF-ER Laboratory Overview / Dietrich Rebholz-Schuhmann [and others].

This book constitutes the refereed proceedings of the 4th International Conference of the CLEF Initiative, CLEF 2013, held in Valencia, Spain, in September 2013. The 32 papers and 2 keynotes presented were carefully reviewed and selected for inclusion in this volume. The papers are organized in topical sections named: evaluation and visualization; multilinguality and less-resourced languages; applications; and Lab overviews.
(source: Nielsen Book Data)

• 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)