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Understanding Natural Language Understanding
About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. “Understanding natural language understanding”, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more.
R 3,663.00
Natural Language Understanding with Python
Build advanced Natural Language Understanding Systems by acquiring data and selecting appropriate technology.Key FeaturesMaster NLU concepts from basic text processing to advanced deep learning techniquesExplore practical NLU applications like chatbots, sentiment analysis, and language translationGain a deeper understanding of large language models like ChatGPTBook DescriptionNatural language understanding (NLU) organizes and structures, language allowing computer systems to effectively process textual information for many different practical applications. Natural Language Understanding with Python will help you explore practical techniques that make use of NLU to build a wide variety of creative and useful applications.Complete with step-by-step explanations of essential concepts and practical examples, this book begins by teaching you about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also be introduced to practical issues such as acquiring data, evaluating systems, and deploying NLU applications, along with their solutions. This book is a comprehensive guide that will help you explore the full spectrum of essential NLU techniques and resources.By the end of this book, you will be familiar with the foundational concepts of NLU, deep learning, and large language models (LLMs). You will be well on your way to having the skills to independently apply NLU technology in your own academic and practical applications.What you will learnThe most important skill that readers will acquire is not just HOW to apply natural language techniques, but WHY to select particular techniques.The book will also cover important practical considerations concerning acquiring real data and evaluating real system performance, not just performing textbook evaluations with pre-existing corpora.After reading this book and studying the code, readers will be equipped to build state of the art as well as practical natural language applications to solve real problems.How to develop and fine-tune an NLP applicationMaintaining NLP applications after deploymentWho this book is forThis book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.
R 1,693.00
Pragmatics and Natural Language Understanding
This book differs from other introductions to pragmatics in approaching the problems of interpreting language use in terms of interpersonal modelling of beliefs and intentions. It is intended to make issues involved in language understanding, such as speech, text, and discourse, accessible to the widest group possible -- not just specialists in linguistics or communication theorists -- but all scholars and researchers whose enterprises depend on having a useful model of how communicative agents understand utterances and expect their own utterances to be understood. Based on feedback from readers over the past seven years, explanations in every chapter have been improved and updated in this thoroughly revised version of the original text published in 1989. The most extensive revisions concern the relevance of technical notions of mutual and normal belief, and the futility of using the notion 'null context' to describe meaning. In addition, the discussion of implicature now includes an extended explication of "Grice's Cooperative Principle" which attempts to put it in the context of his theory of meaning and rationality, and to preclude misinterpretations which it has suffered over the past 20 years. The revised chapter exploits the notion of normal belief to improve the account of conversational implicature.
R 9,363.68
Pragmatics and Natural Language Understanding
This book differs from other introductions to pragmatics in approaching the problems of interpreting language use in terms of interpersonal modelling of beliefs and intentions. It is intended to make issues involved in language understanding, such as speech, text, and discourse, accessible to the widest group possible -- not just specialists in linguistics or communication theorists -- but all scholars and researchers whose enterprises depend on having a useful model of how communicative agents understand utterances and expect their own utterances to be understood. Based on feedback from readers over the past seven years, explanations in every chapter have been improved and updated in this thoroughly revised version of the original text published in 1989. The most extensive revisions concern the relevance of technical notions of mutual and normal belief, and the futility of using the notion 'null context' to describe meaning. In addition, the discussion of implicature now includes an extended explication of "Grice's Cooperative Principle" which attempts to put it in the context of his theory of meaning and rationality, and to preclude misinterpretations which it has suffered over the past 20 years. The revised chapter exploits the notion of normal belief to improve the account of conversational implicature.
R 2,161.00
Naive Semantics for Natural Language Understanding
This book introduces a theory, Naive Semantics (NS), a theory of the knowledge underlying natural language understanding. The basic assumption of NS is that knowing what a word means is not very different from knowing anything else, so that there is no difference in form of cognitive representation between lexical semantics and ency clopedic knowledge. NS represents word meanings as commonsense knowledge, and builds no special representation language (other than elements of first-order logic). The idea of teaching computers common sense knowledge originated with McCarthy and Hayes (1969), and has been extended by a number of researchers (Hobbs and Moore, 1985, Lenat et aI, 1986). Commonsense knowledge is a set of naive beliefs, at times vague and inaccurate, about the way the world is structured. Traditionally, word meanings have been viewed as criterial, as giving truth conditions for membership in the classes words name. The theory of NS, in identifying word meanings with commonsense knowledge, sees word meanings as typical descriptions of classes of objects, rather than as criterial descriptions. Therefore, reasoning with NS represen tations is probabilistic rather than monotonic. This book is divided into two parts. Part I elaborates the theory of Naive Semantics. Chapter 1 illustrates and justifies the theory. Chapter 2 details the representation of nouns in the theory, and Chapter 4 the verbs, originally published as "Commonsense Reasoning with Verbs" (McDowell and Dahlgren, 1987). Chapter 3 describes kind types, which are naive constraints on noun representations.
R 4,648.70
Natural Language Understanding in a Semantic Web Context
This book serves as a starting point for Semantic Web (SW) students and researchers interested in discovering what Natural Language Processing (NLP) has to offer. NLP can effectively help uncover the large portions of data held as unstructured text in natural language, thus augmenting the real content of the Semantic Web in a significant and lasting way. The book covers the basics of NLP, with a focus on Natural Language Understanding (NLU), referring to semantic processing, information extraction and knowledge acquisition, which are seen as the key links between the SW and NLP communities. Major emphasis is placed on mining sentences in search of entities and relations. In the course of this “quest", challenges will be encountered for various text analysis tasks, including part-of-speech tagging, parsing, semantic disambiguation, named entity recognition and relation extraction. Standard algorithms associated with these tasks are presented to provide an understanding of the fundamental concepts. Furthermore, the importance of experimental design and result analysis is emphasized, and accordingly, most chapters include small experiments on corpus data with quantitative and qualitative analysis of the results. This book is divided into four parts. Part I “Searching for Entities in Text” is dedicated to the search for entities in textual data. Next, Part II “Working with Corpora” investigates corpora as valuable resources for NLP work. In turn, Part III “Semantic Grounding and Relatedness” focuses on the process of linking surface forms found in text to entities in resources. Finally, Part IV “Knowledge Acquisition” delves into the world of relations and relation extraction. The book also includes three appendices: “A Look into the Semantic Web” gives a brief overview of the Semantic Web and is intended to bring readers less familiar with the Semantic Web up to speed, so that they too can fully benefit from the material of this book. “NLP Tools and Platforms” provides information about NLP platforms and tools, while “Relation Lists” gathers lists of relations under different categories, showing how relations can be varied and serve different purposes. And finally, the book includes a glossary of over 200 terms commonly used in NLP. The book offers a valuable resource for graduate students specializing in SW technologies and professionals looking for new tools to improve the applicability of SW techniques in everyday life – or, in short, everyone looking to learn about NLP in order to expand his or her horizons. It provides a wealth of information for readers new to both fields, helping them understand the underlying principles and the challenges they may encounter.
R 3,132.54
Integration of World Knowledge for Natural Language Understanding
This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications.First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction.In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.
R 3,586.11
Natural Language Understanding in a Semantic Web Context
This book serves as a starting point for Semantic Web (SW) students and researchers interested in discovering what Natural Language Processing (NLP) has to offer. NLP can effectively help uncover the large portions of data held as unstructured text in natural language, thus augmenting the real content of the Semantic Web in a significant and lasting way. The book covers the basics of NLP, with a focus on Natural Language Understanding (NLU), referring to semantic processing, information extraction and knowledge acquisition, which are seen as the key links between the SW and NLP communities. Major emphasis is placed on mining sentences in search of entities and relations. In the course of this “quest", challenges will be encountered for various text analysis tasks, including part-of-speech tagging, parsing, semantic disambiguation, named entity recognition and relation extraction. Standard algorithms associated with these tasks are presented to provide an understanding of the fundamental concepts. Furthermore, the importance of experimental design and result analysis is emphasized, and accordingly, most chapters include small experiments on corpus data with quantitative and qualitative analysis of the results. This book is divided into four parts. Part I “Searching for Entities in Text” is dedicated to the search for entities in textual data. Next, Part II “Working with Corpora” investigates corpora as valuable resources for NLP work. In turn, Part III “Semantic Grounding and Relatedness” focuses on the process of linking surface forms found in text to entities in resources. Finally, Part IV “Knowledge Acquisition” delves into the world of relations and relation extraction. The book also includes three appendices: “A Look into the Semantic Web” gives a brief overview of the Semantic Web and is intended to bring readers less familiar with the Semantic Web up to speed, so that they too can fully benefit from the material of this book. “NLP Tools and Platforms” provides information about NLP platforms and tools, while “Relation Lists” gathers lists of relations under different categories, showing how relations can be varied and serve different purposes. And finally, the book includes a glossary of over 200 terms commonly used in NLP. The book offers a valuable resource for graduate students specializing in SW technologies and professionals looking for new tools to improve the applicability of SW techniques in everyday life – or, in short, everyone looking to learn about NLP in order to expand his or her horizons. It provides a wealth of information for readers new to both fields, helping them understand the underlying principles and the challenges they may encounter.
R 3,133.43
Naive Semantics for Natural Language Understanding
This book introduces a theory, Naive Semantics (NS), a theory of the knowledge underlying natural language understanding. The basic assumption of NS is that knowing what a word means is not very different from knowing anything else, so that there is no difference in form of cognitive representation between lexical semantics and ency clopedic knowledge. NS represents word meanings as commonsense knowledge, and builds no special representation language (other than elements of first-order logic). The idea of teaching computers common sense knowledge originated with McCarthy and Hayes (1969), and has been extended by a number of researchers (Hobbs and Moore, 1985, Lenat et aI, 1986). Commonsense knowledge is a set of naive beliefs, at times vague and inaccurate, about the way the world is structured. Traditionally, word meanings have been viewed as criterial, as giving truth conditions for membership in the classes words name. The theory of NS, in identifying word meanings with commonsense knowledge, sees word meanings as typical descriptions of classes of objects, rather than as criterial descriptions. Therefore, reasoning with NS represen tations is probabilistic rather than monotonic. This book is divided into two parts. Part I elaborates the theory of Naive Semantics. Chapter 1 illustrates and justifies the theory. Chapter 2 details the representation of nouns in the theory, and Chapter 4 the verbs, originally published as "Commonsense Reasoning with Verbs" (McDowell and Dahlgren, 1987). Chapter 3 describes kind types, which are naive constraints on noun representations.
R 4,649.60
Evolutionary Language Understanding
This book records a unique attempt over a ten-year period to use stochastic optimization in the natural language processing domain. Setting the work against the background of the logical rule-based approach, the author provides a context for understanding the differences in assumptions about the nature of language and cognition.
R 5,591.00
Understanding Language
This textbook is intended for graduate students in computer science and linguistics who are interested in developing expertise in natural language processing (NLP) and in those aspects of artificial intelligence which are concerned with computer models oflanguage comprehension. The text is somewhat different from a number of other excellent textbooks in that its foci are more on the linguistic and psycho linguistic prerequisites and on foundational issues concerning human linguistic behavior than on the description of the extant models and algorithms. The goal is to make the student, undertaking the enormous task of developing computer models for NLP, well aware of the major diffi culties and unsolved problems, so that he or she will not begin the task (as it has often been done) with overoptimistic hopes or claims about the generalizability of models, when such hopes and claims are incon sistent either with some aspects of the formal theory or with known facts about human cognitive behavior. Thus, I try to enumerate and explain the variety of cognitive, linguistic, and pragmatic data which must be understood and formalized before they can be incorporated into a computer model.
R 1,568.42
Ontology-Based Interpretation of Natural Language
For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies
R 1,143.05
Introduction to Natural Language Processing
This book provides a thorough and comprehensive introduction to natural language processing (NLP), a critical field at the intersection of artificial intelligence and computational linguistics. It explores key techniques such as sentiment analysis, which enables the detection of emotional tone in text, machine translation, facilitating the conversion of text between languages, and named entity recognition (NER), which identifies and classifies entities like names, dates, and locations within text data.The book delves into deep learning advancements, particularly the use of neural networks such as transformers and recurrent models, which have revolutionized NLP applications. Readers will gain insights into how these models drive innovations in areas such as text classification, language generation, and speech recognition.In addition to technical concepts, the book also addresses the ethical considerations surrounding NLP, emphasizing the responsible use of AI technologies to mitigate issues like bias, misinformation, and privacy concerns. Practical case studies and real-world examples are included to illustrate how NLP is applied in various sectors, including healthcare, finance, and customer service.This book is an invaluable resource for students, researchers, and industry professionals seeking to understand the foundational concepts, cutting-edge advancements, and broader implications of NLP, equipping them with the knowledge to innovate and apply these technologies effectively in their respective fields.
R 3,114.00
Computational Model of Natural Language Communication
The ideal of using human language to control machines requires a practical theory of natural language communication that includes grammatical analysis of language signs, plus a model of the cognitive agent, with interfaces for recognition and action, an internal database, and an algorithm for reading content in and out. This book offers a functional framework for theoretical analysis of natural language communication and for practical applications of natural language processing.
R 2,015.00
Natural Language Understanding and Intelligent Applications
This book constitutes the joint refereed proceedings of the 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and the 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, held in Kunming, China, in December 2016.The 48 revised full papers presented together with 41 short papers were carefully reviewed and selected from 216 submissions. The papers cover fundamental research in language computing, multi-lingual access, web mining/text mining, machine learning for NLP, knowledge graph, NLP for social network, as well as applications in language computing.
R 4,381.82
Artificial Intelligence for Natural Language Processing
Artificial Intelligence for Natural Language Processing offers a comprehensive exploration of how advanced computational methods are transforming the way machines understand human language. This book delves into the core principles of Natural Language Processing through an engaging progression – from fundamental word‑level analysis to complex discourse and pragmatic analysis – integrating linguistic theory with cutting‑edge Artificial Intelligence methodologies. It provides a robust framework for both the theoretical underpinnings and practical applications of NLP, ensuring that readers gain a clear understanding of how computers can effectively process and interpret human language.What sets this book apart is its methodical structure that guides the reader through each level of language analysis, building upon earlier chapters to culminate in a deep integration of artificial intelligence within NLP systems. The detailed explanations and examples are designed to bridge the gap between abstract theory and real‑world application, making it an invaluable resource for anyone looking to grasp the nuances of language processing.FEATURESProvides a step‑by‑step progression from word‑level analysis to syntactic, semantic, and pragmatic processingOffers in‑depth discussions on word sense disambiguation with illustrative examplesPresents an exploration of discourse integration and contextual meaning essential for modern NLP modelsDelivers comprehensive coverage of AI applications in NLP, highlighting state‑of‑the‑art computational techniquesSuggests clear, accessible explanations suitable for both beginners and advanced practitionersThis book is ideal for graduate students, researchers, and professionals in computer science, linguistics, and artificial intelligence. Whether you are a seasoned researcher looking to deepen your understanding or a newcomer eager to explore the field, Artificial Intelligence for Natural Language Processing serves as both an essential academic resource and a practical guide for navigating the evolving landscape of language technology.
R 2,694.50
Linguistic Fundamentals for Natural Language Processing
Many NLP tasks have at their core a subtask of extracting the dependencies—who did what to whom—from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications. The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems.Table of Contents: Acknowledgments / Introduction/motivation / Morphology: Introduction / Morphophonology / Morphosyntax / Syntax: Introduction / Parts of speech / Heads, arguments, and adjuncts / Argument types and grammatical functions / Mismatches between syntactic position and semantic roles / Resources / Bibliography / Author's Biography / General Index / Index of Languages
R 1,605.20
Natural Language Processing
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
R 3,045.00
Hands-On Python Natural Language Processing
Get well-versed with traditional as well as modern natural language processing concepts and techniquesKey FeaturesPerform various NLP tasks to build linguistic applications using Python librariesUnderstand, analyze, and generate text to provide accurate resultsInterpret human language using various NLP concepts, methodologies, and toolsBook DescriptionNatural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding.This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own.By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.What you will learnUnderstand how NLP powers modern applicationsExplore key NLP techniques to build your natural language vocabularyTransform text data into mathematical data structures and learn how to improve text mining modelsDiscover how various neural network architectures work with natural language dataGet the hang of building sophisticated text processing models using machine learning and deep learningCheck out state-of-the-art architectures that have revolutionized research in the NLP domainWho this book is forThis NLP Python book is for anyone looking to learn NLP’s theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.
R 1,498.00
Natural Language Processing Fundamentals
Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems.Key FeaturesAssimilate key NLP concepts and terminologies Explore popular NLP tools and techniquesGain practical experience using NLP in application codeBook DescriptionIf NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.What you will learnObtain, verify, and clean data before transforming it into a correct format for usePerform data analysis and machine learning tasks using PythonUnderstand the basics of computational linguisticsBuild models for general natural language processing tasksEvaluate the performance of a model with the right metricsVisualize, quantify, and perform exploratory analysis from any text dataWho this book is forNatural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.
R 1,498.00
Development of Natural Language Processing
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.
R 1,209.00
Connectionist Natural Language Processing
Connection science is a new information-processing paradigm which attempts to imitate the architecture and process of the brain, and brings together researchers from disciplines as diverse as computer science, physics, psychology, philosophy, linguistics, biology, engineering, neuroscience and AI. Work in Connectionist Natural Language Processing (CNLP) is now expanding rapidly, yet much of the work is still only available in journals, some of them quite obscure. To make this research more accessible this book brings together an important and comprehensive set of articles from the journal CONNECTION SCIENCE which represent the state of the art in Connectionist natural language processing; from speech recognition to discourse comprehension. While it is quintessentially Connectionist, it also deals with hybrid systems, and will be of interest to both theoreticians as well as computer modellers. Range of topics covered: Connectionism and Cognitive Linguistics Motion, Chomsky's Government-binding Theory Syntactic Transformations on Distributed Representations Syntactic Neural Networks A Hybrid Symbolic/Connectionist Model for Understanding of Nouns Connectionism and Determinism in a Syntactic Parser Context Free Grammar Recognition Script Recognition with Hierarchical Feature Maps Attention Mechanisms in Language Script-Based Story Processing A Connectionist Account of Similarity in Vowel Harmony Learning Distributed Representations Connectionist Language Users Representation and Recognition of Temporal Patterns A Hybrid Model of Script Generation Networks that Learn about Phonological Features Pronunciation in Text-to-Speech Systems
R 1,570.63
Integrated Natural Language Dialogue
Natural language dialogue is a continuous, unified phenomenon. Speakers use their conversational context to simplify individual utterances through a number of linguistic devices, including ellipsis and definite references. Yet most computational systems for using natural language treat individual utterances as separate entities, and have distinctly separate processes for handling ellipsis, definite references, and other dialogue phenomena. This book, a slightly revised version of the Ph. D. dissertation that I completed in December 1986, describes a different approach. It presents a computational system, Psli3, that uses the uniform framework of a production system architecture to carry out natural language understanding and generation in a well-integrated way. This is demonstrated primarily through intersentential ellipsis resolution, in addition to examples of definite reference resolution and interactive error correction. The system's conversational context arises naturally as the result of the persistence of the internal representations of previous utterances in working memory. Natural language input is interpreted within this framework using a modification of the syntactic technique of chart parsing, extended to include semantics, and adapted to the production system architecture. This technique, called semantic chart parsing, provides a graceful way of handling ambiguity within this architecture, and allows separate knowledge sources to interact smoothly across different utterances in a highly integrated fashion. xvi Integrated Natural Language Dialogue The design of this system demonstrates how flexible and natural user interactions can be carried out using a system with a naturally flexible control structure.
R 4,638.87
Natural Language and Logic
This volume contains the papers presented at the International Scientific Symposium "Natural Language and Logic" held in Hamburg in May 1989. The aim of the papers is to present and discuss latest developments in the application of logic-based meth- ods for natural language understanding. Logic-based methods have gained in importance in the field of computational linguistics as well as for representing various types of knowledge in natural language understanding systems. The volume gives an overview of recent results achieved within the LILOG project (LInguistic and LOgic methods for understanding German texts) - one of the largest research projects in the field of text understanding - as well as within related natural language understanding systems.
R 1,551.22
Spatial Language Understanding
This book provides an overview of multiple aspects of spatial language understanding and explores recent trends of modeling based on very large foundational models and their applications. The authors address the following five main themes: spatial semantic representations in both symbolic and sub-symbolic spaces; spatial information extraction computational models; reasoning over spatial language; commonsense spatial understanding; and multiple modern and state-of-the-art downstream applications of spatial language understanding including dialogue systems, narrative discourse, and grounding language in the physical world with the multi-modal problem settings. One of the essential functions of natural language is to express spatial relationships between objects. Linguistic constructs can encode highly complex structures of objects, spatial relations between them, and patterns of motion through space relative to a reference point. The complexity of spatial language understanding and its importance in downstream tasks that involve grounding the language in the physical world has become evident and important to the natural language processing research community. In addition, this topic has recently attracted the attention of various sub-communities in the intersection of natural language, computer vision, and robotics.
R 1,809.85
Natural Language Processing with Python
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: * Extract information from unstructured text, either to guess the topic or identify "named entities" * Analyze linguistic structure in text, including parsing and semantic analysis * Access popular linguistic databases, including WordNet and treebanks * Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library.If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
R 2,076.00
Introduction to Natural Language Processing Through Prolog
Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing.Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided.An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.
R 3,436.26
Natural Language Processing Workshop
Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text dataKey FeaturesGet familiar with key natural language processing (NLP) concepts and terminologyExplore the functionalities and features of popular NLP toolsLearn how to use Python programming and third-party libraries to perform NLP tasksBook DescriptionDo you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before?With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.What you will learnObtain, verify, clean and transform text data into a correct format for useUse methods such as tokenization and stemming for text extractionDevelop a classifier to classify comments in Wikipedia articlesCollect data from open websites with the help of web scrapingTrain a model to detect topics in a set of documents using topic modelingDiscover techniques to represent text as word and document vectorsWho this book is forThis book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
R 1,498.00
Introduction to Natural Language Processing Through Prolog
Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing.Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided.An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.
R 7,327.00
Artificial Intelligence for Natural Language Processing
Artificial Intelligence for Natural Language Processing offers a comprehensive exploration of how advanced computational methods are transforming the way machines understand human language. This book delves into the core principles of Natural Language Processing through an engaging progression – from fundamental word‑level analysis to complex discourse and pragmatic analysis – integrating linguistic theory with cutting‑edge Artificial Intelligence methodologies. It provides a robust framework for both the theoretical underpinnings and practical applications of NLP, ensuring that readers gain a clear understanding of how computers can effectively process and interpret human language.What sets this book apart is its methodical structure that guides the reader through each level of language analysis, building upon earlier chapters to culminate in a deep integration of artificial intelligence within NLP systems. The detailed explanations and examples are designed to bridge the gap between abstract theory and real‑world application, making it an invaluable resource for anyone looking to grasp the nuances of language processing.FEATURESProvides a step‑by‑step progression from word‑level analysis to syntactic, semantic, and pragmatic processingOffers in‑depth discussions on word sense disambiguation with illustrative examplesPresents an exploration of discourse integration and contextual meaning essential for modern NLP modelsDelivers comprehensive coverage of AI applications in NLP, highlighting state‑of‑the‑art computational techniquesSuggests clear, accessible explanations suitable for both beginners and advanced practitionersThis book is ideal for graduate students, researchers, and professionals in computer science, linguistics, and artificial intelligence. Whether you are a seasoned researcher looking to deepen your understanding or a newcomer eager to explore the field, Artificial Intelligence for Natural Language Processing serves as both an essential academic resource and a practical guide for navigating the evolving landscape of language technology.
R 7,553.53
Explainable Natural Language Processing
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
R 2,037.97
Knowledge and Learning in Natural Language
This work presents a new theory of how children acquire language and discusses its implications for a wide range of topics. It explores the roles of innateness and experience in language acquisition, provides further evidence for the theory of Universal Grammar, and shows how linguistic development in children is a driving force behind language shifts and changes Charles Yang surveys a wide range of errors in children's language and identifies overlooked patterns. He combines these with work in biological evolution in order to develop a model of language acquisition by which to understand the interaction between children's internal linguistic knowledge and their external linguistic experience. He then presents evidence from his own and others' research in the acquisition of syntax and morphology and data from historical language change to test its validity The model is makes quantitative and cross-linguistic predictions about child language. It may also be deployed as a predictive model of language change which, when the evidence is available, could explain why grammars change in a particular direction at a particular time.
R 3,055.00
Language Intelligence
Thorough review of foundational concepts and advanced techniques in natural language processing (NLP) and its impact across sectors Supported by examples and case studies throughout, Language Intelligence provides an in-depth exploration of the latest advancements in natural language processing (NLP), offering a unique blend of insight on theoretical foundations, practical applications, and future directions in the field. Comprised of 10 chapters, this book provides a thorough understanding of both foundational concepts and advanced techniques, starting with an overview of the historical development of NLP and essential mechanisms of Natural Language Understanding (NLU) and Natural Language Generation (NLG). It delves into the data landscape crucial for NLP, emphasizing ethical considerations, and equips readers with fundamental text processing techniques. The book also discusses linguistic features central to NLP and explores computational and cognitive approaches that enrich the field’s advancement. Practical applications and advanced processing techniques across various sectors like healthcare, legal, finance, and education are showcased, along with a critical examination of NLP metrics and methods for evaluation. The appendices offer detailed explorations of text representation methods, advanced applications, and Python’s NLP capabilities, aiming to inform, inspire, and ignite a passion for NLP in the ever-expanding digital universe. Written by a highly qualified academic with significant research experience in the field, Language Intelligence covers topics including: Fundamental text processing, covering text cleaning, sentence splitting, tokenization, lemmatization and stemming, stop-word removal, part-of-speech tagging, and parsing and syntactic analysisComputational and cognitive approaches, covering human-like reasoning, transfer learning, and learning with minimal examplesAffective, psychological, and content analysis, covering sentiment analysis, emotion recognition, irony, humour, and sarcasm detection, and indicators of distressMultilingual natural language processing, covering translation and transliteration, cross-lingual models and embeddings, low-resource language processing, and cultural nuance and idiom recognition Language Intelligence is an ideal reference for professionals across sectors and graduate students in related programs of study who have a foundational understanding of computer science, linguistics, and artificial intelligence looking to delve deeper into the intricacies of NLP.
R 4,559.00
Knowledge and Learning in Natural Language
This work presents a new theory of how children acquire language and discusses its implications for a wide range of topics. It explores the roles of innateness and experience in language acquisition, provides further evidence for the theory of Universal Grammar, and shows how linguistic development in children is a driving force behind language shifts and changes Charles Yang surveys a wide range of errors in children's language and identifies overlooked patterns. He combines these with work in biological evolution in order to develop a model of language acquisition by which to understand the interaction between children's internal linguistic knowledge and their external linguistic experience. He then presents evidence from his own and others' research in the acquisition of syntax and morphology and data from historical language change to test its validity The model is makes quantitative and cross-linguistic predictions about child language. It may also be deployed as a predictive model of language change which, when the evidence is available, could explain why grammars change in a particular direction at a particular time.
R 8,507.00
Natural Language Processing
This book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, the and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms.
R 9,958.93
Thai Natural Language Processing
This book presents comprehensive solutions for readers wanting to develop their own Natural Language Processing projects for the Thai language. Starting from the fundamental principles of Thai, it discusses each step in Natural Language Processing, and the real-world applications. In addition to theory, it also includes practical workshops for readers new to the field who want to start programming in Natural Language Processing. Moreover, it features a number of new techniques to provide readers with ideas for developing their own projects. The book details Thai words using phonetic annotation and also includes English definitions to help readers understand the content.
R 4,252.89
Structure Discovery in Natural Language
Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process?After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction.The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.
R 3,021.76
Understanding Language
It is widely recognized that language is humanity’s most distinctive and valuable faculty. In this work, originally published in 1974, Roger Fowler explains the character and absorbing interest of language. Designed as an introductory text for students and others concerned with human communication, the book is clearly and concisely written, yet it in no way oversimplifies its rich and complicated subject.The opening chapters set the scene by a discussion of the power of language in the social and psychological life of a man, while the main body of the book is an introduction to linguistics, the science of language study. Coverage is provided of the main topics in linguistic description – semantics, syntax, phonetics – as well as of the functions of language, its status in society and its relation to the individual. The reader is invited to participate in some advanced thinking within an up-to-date and consistent linguistic theory. Particular attention is given to the individual as language-learner, since the process of language acquisition illuminates most clearly the naturalness and the complexity of language.The author’s arguments are illustrated with hundreds of examples from English and other languages. Suggestions for further reading are included in the exposition, and the reader who follows the arguments and pursues the carefully arranged bibliographical recommendations will acquire a substantial insight into contemporary linguistics – the most important and advanced of the modern human sciences.
R 1,685.00
Natural Language Information Retrieval
The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa tion access has exploded. Emerging applications in computer-assisted infor mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.
R 3,056.42
Thai Natural Language Processing
This book presents comprehensive solutions for readers wanting to develop their own Natural Language Processing projects for the Thai language. Starting from the fundamental principles of Thai, it discusses each step in Natural Language Processing, and the real-world applications. In addition to theory, it also includes practical workshops for readers new to the field who want to start programming in Natural Language Processing. Moreover, it features a number of new techniques to provide readers with ideas for developing their own projects. The book details Thai words using phonetic annotation and also includes English definitions to help readers understand the content.
R 4,481.00
Coarse-to-Fine Natural Language Processing
The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions.This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing.Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach.Eugene Charniak (Brown University)
R 4,030.00
Text Understanding in LILOG
The IBM project LILOG presented in this volume represents afundamental stepbeyond computer science as hithertounderstood. It was a successful project in every respect andhas shed light on conjectured basic interrelations betweenknowledge processing and language definition. Knowledgeprocessing is strongly coupled to the natural language used,and for applied knowledge processing an information base isneededwhich defines the semantic contents andinterrelations of the language.The LILOG project was an implementation of an informationbasein the German language. A set of tools was alsodeveloped to work with the system, including structuredman-machine interfaces using natural language, inferencealgorithms, and a complete subsystem to acquire and storethe required knowledge.The LILOG project started in 1985 and a functional systemwas demonstrated in 1991. The project involved approximately200 of the scientists working in Germany in the fields ofcomputational linguistics, natural language understandingsystems, and artificial intelligence. The project provesthat a cooperative project between universities and industrycan produce useful results both in pure research and inimplemented methods and tools.
R 1,629.83
Building Natural Language Generation Systems
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.
R 5,624.09
