This book addresses the issue of how the user's level of domain knowledge affects interaction with a computer system. It demonstrates the feasibility of incorporating a model of user's domain knowledge into a natural language generation system.

Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book * Break text down into its component parts for spelling correction, feature extraction, and phrase transformation * Work through NLP concepts with simple and easy-to-follow programming recipes * Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn * The scope of natural language complexity and how they are processed by machines * Clean and wrangle text using tokenization and chunking to help you process data better * Tokenize text into sentences and sentences into words * Classify text and perform sentiment analysis * Implement string matching algorithms and normalization techniques * Understand and implement the concepts of information retrieval and text summarization * Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: * NTLK essentials by Nitin Hardeniya * Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins * Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.

Never digitized before, this new technique of text data mining discovers means to organize and interpret psychology of the writer in an objective way. Using Computational Intelligence algorithms to mine textual data, the book presents case studies of books especially the ideal text of scriptures to get a dissection of the author's main objective behind the book through the communicative lens of the reader.

Spoken Dialogue Systems Technology and Design covers key topics in the field of spoken language dialogue interaction from a variety of leading researchers. It brings together several perspectives in the areas of corpus annotation and analysis, dialogue system construction, as well as theoretical perspectives on communicative intention, context-based generation, and modelling of discourse structure. These topics are all part of the general research and development within the area of discourse and dialogue with an emphasis on dialogue systems; corpora and corpus tools and semantic and pragmatic modelling of discourse and dialogue.

"Advances in Non-Linear Modeling for Speech Processing" includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition.
Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle.
The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed.
Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set.

ThoughtTreasure is a commonsense knowledge base and architecture for natural language processing. It uses multiple representations including logic, finite automata, grids, and scripts. The ThoughtTreasure architecture consists of: the text agency, containing text agents for recognizing words, phrases, and names, and mechanisms for learning new words and inflections; the syntactic component, containing a syntactic parser, base rules, and filters; the semantic component, containing a semantic parser for producing a surface-level understanding of a sentence, a natural language generator, and an anaphoric parser for resolving anaphoric entities such as pronouns; the planning agency, containing planning agents for achieving goals on behalf of simulated actors; and the understanding agency, containing understanding agents for producing a more detailed understanding of a discourse.

What are mental concepts? Why do they work the way they do? How can they be captured in language? How can they be captured in a computer? The authors describe the development of, and clearly explain, the underlying linguistic theory and the working software they have developed over 40 years to store declarative knowledge in a computer fully to the same level as language, knowledge accessible via ordinary conversation. During this 40 year project there was no epiphany, no "Eureka moment," except perhaps for the day that their parser program successfully parsed a long sentence for the first time, taking into account the contribution of every word and punctuation mark. Their parser software can now parse a whole paragraph of long sentences each comprising multiple subordinate clauses with punctuation, to determine the paragraph's global meaning. Among many practical applications for their technology is precision communication with the Internet. The authors show that knowledge stored in language is not unstructured as is generally assumed. Rather they show that language expressions are highly structured once the rules of syntax are understood. Lexical words, grammaticals, punctuation marks, paragraphs and poetry, single elimination tournaments, "grandmother cells," calculator algorithms are just a few of the topics explored in this smart, witty, and eclectic tour through natural language understanding by a computer. Illustrated with flow-of-meaning-trees and easily followed Mensa tables this essay outlines a wide-ranging theory of language and thought and its transition to computers. John W. Gorman, a Masters in Engineering from the University of Auckland, joined his father, John G. Gorman, Lasker Award winning medical researcher, in their enterprise twenty years ago to solve the until now intractable problem of computer understanding of thought and language. An Essay Concerning Computer Understanding will provoke linguists, neuroscientists, software designers, advertisers, poets, and the just plain curious. The book suggests many opportunities for future research in linguistic theory and cognitive science employing hands on experiments with computer models of knowledge and the brain. Discover the theory and practice of computer understanding that has computational linguists everywhere taking notice.

For 50 years the natural language interface has tempted and challenged researchers and the public in equal measure. As advanced domains such as robotic systems mature over the next ten years, the need for effective language interfaces will become more significant as the disparity between physical and language ability becomes more evident. Natural language conversation with robots and other situated systems will not only require a clear understanding of theories of language use, models of spatial representation and reasoning, and theories of intentional action and agency - but will also require that all of these models be made accessible within tractable dialogue processing frameworks. While such issues pose research questions which are significant, particularly when we consider them in the light of the many other challenges in language processing and spatial theory, the benefits of competence in situated dialogue to the fields of robotics, geographic information systems, game design, and applied artificial intelligence cannot be underestimated. This book examines the burgeoning field of Situated Dialogue Systems and describes for the first time a complete computational model of situated dialogue competence for practical dialogue systems. The book can be broadly broken down into two parts. The first three chapters examine on one hand the issues which complicate the computational modelling of situated dialogue, i.e., issues of agency and spatial language competence, and on the other hand examines theories of dialogue modelling and management with respect to the needs of the situated domain. The second part of the book then details a situated dialogue processing architecture. Novel features of this architecture include the modular integration of an intentionality model alongside an exchange-structure based organization of discourse, plus the use of a functional contextualization process that operates over both implicit and explicit content in user contributions. The architecture is described at a course level, but in sufficient detail for others to use as a starting point in their own explorations of situated language intelligence.

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

In the 14 years since its ?rst edition back in 1997, the European Conference on Research and Advanced Technology for Digital Libraries (ECDL) has become the reference meeting for an interdisciplinary community of researchers and practitioners whose professional activities revolve around the theme of d- th ital libraries. This volume contains the proceedings of ECDL 2010, the 14 conference in this series, which, following Pisa (1997), Heraklion (1998), Paris (1999),Lisbon(2000),Darmstadt(2001),Rome(2002),Trondheim(2003),Bath (2004), Vienna (2005), Alicante (2006), Budapest (2007), Aarhus (2008), and Corfu (2009), was held in Glasgow, UK, during September 6-10, 2010. th Asidefrombeingthe14 edition of ECDL, this was also the last, at least with this name since starting with 2011, ECDL will be renamed (so as to avoid acronym con?icts with the European Computer Driving Licence) to TPLD, standing for the Conference on Theory and Practice of Digital Libraries. We hope you all will join us for TPDL 2011 in Berlin! For ECDL 2010 separate calls for papers, posters and demos were issued, - sulting in the submission to the conference of 102 full papers, 40 posters and 13 demos. This year, for the full papers, ECDL experimented with a novel, two-tier reviewing model, with the aim of further improving the quality of the resu- ing program. A ?rst-tier Program Committee of 87 members was formed, and a further Senior Program Committee composed of 15 senior members of the DL community was set up.

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Within the rapidly-growing arena of 'virtual worlds', such as Massively Multiplayer Online Games (MMOs), individuals behave in particular ways, influence one another, and develop complex relationships. This setting can be a useful tool for modeling complex social systems, cognitive factors, and interactions between groups and within organizations. To study these worlds effectively requires a cross-disciplinary approach that integrates social science theories with big data analytics. This broad-based book offers a comprehensive and holistic perspective on the field. It brings together research findings from an international team of experts in computer science (artificial intelligence, game design, and social computing), psychology, and the social sciences to help researchers and practitioners better understand the fundamental processes underpinning social behavior in virtual worlds such as World of Warcraft, Rift, Eve Online, and Travian.

Natural Language Processing as a Foundation of the Semantic Web argues that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web, in several different ways, and whether its advocates realise this or not. Chiefly, it argues, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels based on lower level empirical computations over usage. The claim being made is definitely not logic-bad, NLP-good in any simple-minded way, but that the SW will be a fascinating interaction of these two methodologies, like the WWW (which, as the authors explain, has been a fruitful field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite resource description framework (RDF) knowledge stores for the SW from existing WWW (unstructured) text databases, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. It is also assumed here that, whatever the limitations on current SW representational power drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable. Natural Language Processing as a Foundation of the Semantic Web will appeal to researchers, practitioners and anyone with an interest in NLP, the philosophy of language, cognitive science, the Semantic Web and Web Science generally, as well as providing a magisterial and controversial overview of the history of artificial intelligence

The essays in this interdisciplinary book cover a range of implementations and designs, from formal computational models to large-scale NL processing systems.Natural language (NL) refers to human language-complex, irregular, diverse, with all its philosophical problems of meaning and context. Setting a new direction in AI research, this book explores the development of knowledge representation and reasoning (KRR) systems that simulate the role of NL in human information and knowledge processing. Traditionally, KRR systems have incorporated NL as an interface to an expert system or knowledge base that performed tasks separate from NL processing. As this book shows, however, the computational nature of representation and inference in NL makes it the ideal level for all tasks in an intelligent computer system. NL processing combines the qualitative characteristics of human knowledge processing with a computer's quantitative advantages, allowing for in-depth, systematic processing of vast amounts of information. The essays in this interdisciplinary book cover a range of implementations and designs, from formal computational models to large-scale NL processing systems.ContributorsSyed S. Ali, Bonnie J. Dorr, Karen Ehrlich, Robert Givan, Susan M. Haller, Sanda Harabagiu, Chung Hee Hwang, Lucja Iwanska, Kellyn Kruger, Naveen Mata, David A. McAllester, David D. McDonald, Susan W. McRoy, Dan Moldovan, William J. Rapaport, Lenhart Schubert, Stuart C. Shapiro, Clare R. Voss

In "Speaking," Willem "Pim" Levelt, Director of the Max-Planck-Institut fur Psycholinguistik, accomplishes the formidable task of covering the entire process of speech production, from constraints on conversational appropriateness to articulation and self-monitoring of speech. Speaking is unique in its balanced coverage of all major aspects of the production of speech, in the completeness of its treatment of the entire speech process, and in its strategy of exemplifying rather than formalizing theoretical issues."