Conferences related to Natural Language Processing

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2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)

Theory, development, application and experience, gained through experimentation or reasoned analogies in information retrieval and knowledge management

  • 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP)

    Theory, development, application, and experience, gained through experimentation or reasoned analogies in information retrieval and knowledge management

  • 2012 International Conference on Information Retrieval & Knowledge Management (CAMP)

    INFORMATION RETRIEVAL IR Theory and Formal Models Cross-language retrieval, Multilingual retrieval, Machine translation for IR Web IR and Social media search Semi-structured information retrieval and Semantic search Interactive IR, Task-based IR User studies and models, User interfaces and visualization Multimedia IR KNOWLEDGE MANAGEMENT Knowledge engineering Digital Libraries, enterprise search, intranet search, and desktop search Social network Knowledge Intelligence Knowledge and privacy Knowledge synthesis and visualization Web and Social Knowledge Management Metadata and structured documents COMPUTATIONAL LINGUISTIC Text representation, text analysis, text summarization Collaborative filtering Text Categorization Text Clustering Question answering, Lexical acquisition Dialog System Machine Translation

  • 2010 International Conference on Information Retrieval & Knowledge Management (CAMP)

    CAMP'10 is an international scientific conference for research in the field of information retrieval and knowledge management. Information retrieval is the art and science of retrieving from a collection of items a subset that serves the user s purpose. While, knowledge management comprises a range of practices used in an organization to identify, create, represent, distribute and enable adoption of insights and experiences. The two areas intertwined as information retrieval provides means of knowledge i


2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

The conference serves as the primary annual meeting for researchers in the field of human-robot interaction. The event will include a main papers track and additional sessions for posters, demos, and exhibits. Additionally, the conference program will include a full day of workshops and tutorials running in parallel.

  • 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchersin robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior,anthropology, and many other fields.

  • 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    This conference focuses on the interaction between humans and robots.

  • 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very bestresearch and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary,reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificialintelligence, organizational behavior, anthropology, and many other fields.

  • 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single-track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics.

  • 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    TOPICS: Robot companions, Lifelike robots, Assistive (health & personal care) robotics, Remote robots, Mixed initiative interaction, Multi-modal interaction, Long-term interaction with robots, Awareness and monitoring of humans, Task allocation and coordination, Autonomy and trust, Robot-team learning, User studies of HRI, Experiments on HRI collaboration, Ethnography and field studies, HRI software architectures

  • 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    * Robot companions * Lifelike robots * Assistive (health & personal care) robotics * Remote robots * Mixed initiative interaction * Multi-modal interaction * Long-term interaction with robots * Awareness and monitoring of humans * Task allocation and coordination * Autonomy and trust * Robot-team learning * User studies of HRI * Experiments on HRI collaboration * Ethnography and field studies * HRI software architectures

  • 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics Individual vs. group HRI

  • 2007 2nd Annual Conference on Human-Robot Interaction (HRI)


2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

The conference focuses on all areas of machine learning and its applications in medicine, biology, industry, manufacturing, security, education, virtual environments, game playing big data, deep learning, and problem solving.


2017 29th Chinese Control And Decision Conference (CCDC)

Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2018 Chinese Control And Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2016 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create aforum for scientists, engineers and practitioners throughout the world to present the latestadvancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2015 27th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2014 26th Chinese Control And Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create aforum for scientists, engineers and practitioners throughout the world to present the latestadvancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2013 25th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2012 24th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2011 23rd Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2010 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies

  • 2009 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2008 Chinese Control and Decision Conference (CCDC)


2017 40th International Conference on Telecommunications and Signal Processing (TSP)

The TSP 2017 Conference is organized by fifteen universities from Czech Republic, Hungary, Turkey, Taiwan, Japan, Slovak Republic, Spain, Bulgaria, France, Slovenia, Croatia, and Poland, for academics, researchers, and developers and it serves as a premier annual international forum to promote the exchange of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors from the whole Europe, America, Asia, Australia, and Africa. The international expansion motivates the organizers in their effort to providing a platform for exchanging information and experience to help to improve the level and extent of scientific cooperation between university students, academics, and employees of research centers.

  • 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

    The TSP 2018 Conference is organized by seventeen universities from Czech Rep., Hungary, Turkey, Taiwan, Japan, Slovak Rep., Spain, Bulgaria, France, Slovenia, Croatia, Greece, and Poland, for academics, researchers, and developers and it serves as a premier annual international forum to promote the exchange of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors from the whole Europe, America, Asia, Australia, and Africa. The international expansion motivates the organizers in their effort to providing a platform for exchanging information and experience to help to improve the level and extent of scientific cooperation between university students, academics, and employees of research centers.

  • 2016 39th International Conference on Telecommunications and Signal Processing (TSP)

    The 2016 39th International Conference on Telecommunications and Signal Processing (TSP) is organized for academics, researchers, and developers and it serves as a forum to promote the exchange of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors from the whole Europe, America, Asia, Australia, and Africa. The international expansion motivates the organizers in their effort to providing a platform for exchanging information and experience to help to improve the level and extent of scientific cooperation between university students, academics, and employees of research centers.

  • 2015 38th International Conference on Telecommunications and Signal Processing (TSP)

    The 2015 38th International Conference on Telecommunications and Signal Processing (TSP) is organized for academics, researchers, and developers and it serves as a forum to promote the exchange of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors from the whole Europe, America, Asia, Australia, and Africa. The international expansion motivates the organizers in their effort to providing a platform for exchanging information and experience to help to improve the level and extent of scientific cooperation between university students, academics, and employees of research centers.

  • 2013 36th International Conference on Telecommunications and Signal Processing (TSP)

    The TSP 2013 conference is organized for, but not limited to, young academics, researchers and developers from different branches of telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists and developers, to meet new colleagues, collect new ideas and establish new cooperation between research groups. The conference originally started as a Central European initiative but recently we have had participants from Universities and Research Centers from the whole Europe and also from Asia, America and Africa.

  • 2012 35th International Conference on Telecommunications and Signal Processing (TSP)

    The TSP 2012 conference is organized for young academics, researchers and developers from different branches of telecommunication technology and signal processing.

  • 2011 34th International Conference on Telecommunications and Signal Processing (TSP)

    The TSP 2011 conference is organized for, but not limited to, young academics, researchers and developers from different branches of telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists and developers, to meet new colleagues, collect new ideas and establish new cooperation between research groups. The conference originally started as a Central European initiative but recently we have had participants from Universities and Research Centers from the whole Europe and also from Asia, America and Africa.


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Periodicals related to Natural Language Processing

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Intelligent Systems, IEEE

IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...


Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...


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Most published Xplore authors for Natural Language Processing

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Xplore Articles related to Natural Language Processing

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Indonesian shift-reduce constituent parser

Mario Filino; Ayu Purwarianti 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Syntactic information, obtainable through syntactical analysis, plays an important role in many areas of NLP. Researches of Indonesian constituent parser have been very limited with the currently available yields very poor performance of 38.89% and 47.22% using Earley and CYK algorithm respectively. With the availability of the newly introduced Indonesian treebank corpus, we evaluate the performance of Indonesian constituent parser ...


Video Event Understanding Using Natural Language Descriptions

Vignesh Ramanathan; Percy Liang; Li Fei-Fei 2013 IEEE International Conference on Computer Vision, 2013

Human action and role recognition play an important part in complex event understanding. State-of-the-art methods learn action and role models from detailed spatio temporal annotations, which requires extensive human effort. In this work, we propose a method to learn such models based on natural language descriptions of the training videos, which are easier to collect and scale with the number ...


Indonesian essay grading module using Natural Language Processing

Try Ajitiono; Yani Widyani 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Moodle is a LMS application which serves as an online courses platform. As for now, Moodle doesn't have capability to grade essay question automatically, so teachers should grade all the submitted answers by students one by one. This is a problem for teachers because when two sentences are being compared, they don't only compare them syntactically, but also semantically. Our ...


Spoken dialog system framework supporting multiple concurrent sessions

Muhammad Qasim; Sarmad Hussain; Tania Habib; Shafiq Ur Rahman 2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA), 2016

Spoken dialog systems are becoming increasingly popular to provide information to people. Different frameworks are available that provide the necessary infrastructure to develop a dialog system for any domain and language. The support for multiple concurrent sessions is a major requirement of real-world dialog systems. This paper proposes a framework that supports multiple concurrent sessions for developing dialog systems. The ...


HMM based approach for Online Arabic Handwriting recognition

Hozeifa Adam Abd Alshafy; Mohamed Elhafiz Mustafa 2014 14th International Conference on Intelligent Systems Design and Applications, 2014

This paper presents a novel system approach for online Arabic handwriting recognition. The approach segments the word using new character boundaries detection based algorithm. Moreover it employs HMM-based classification method for the recognition. A dataset: Sudan University of Science and Technology Online Arabic Handwriting (SUSTOLAH) is used in testing the proposed approach. Promising experimental results of testing the approach with ...


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Educational Resources on Natural Language Processing

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eLearning

Indonesian shift-reduce constituent parser

Mario Filino; Ayu Purwarianti 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Syntactic information, obtainable through syntactical analysis, plays an important role in many areas of NLP. Researches of Indonesian constituent parser have been very limited with the currently available yields very poor performance of 38.89% and 47.22% using Earley and CYK algorithm respectively. With the availability of the newly introduced Indonesian treebank corpus, we evaluate the performance of Indonesian constituent parser ...


Video Event Understanding Using Natural Language Descriptions

Vignesh Ramanathan; Percy Liang; Li Fei-Fei 2013 IEEE International Conference on Computer Vision, 2013

Human action and role recognition play an important part in complex event understanding. State-of-the-art methods learn action and role models from detailed spatio temporal annotations, which requires extensive human effort. In this work, we propose a method to learn such models based on natural language descriptions of the training videos, which are easier to collect and scale with the number ...


Indonesian essay grading module using Natural Language Processing

Try Ajitiono; Yani Widyani 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Moodle is a LMS application which serves as an online courses platform. As for now, Moodle doesn't have capability to grade essay question automatically, so teachers should grade all the submitted answers by students one by one. This is a problem for teachers because when two sentences are being compared, they don't only compare them syntactically, but also semantically. Our ...


Spoken dialog system framework supporting multiple concurrent sessions

Muhammad Qasim; Sarmad Hussain; Tania Habib; Shafiq Ur Rahman 2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA), 2016

Spoken dialog systems are becoming increasingly popular to provide information to people. Different frameworks are available that provide the necessary infrastructure to develop a dialog system for any domain and language. The support for multiple concurrent sessions is a major requirement of real-world dialog systems. This paper proposes a framework that supports multiple concurrent sessions for developing dialog systems. The ...


HMM based approach for Online Arabic Handwriting recognition

Hozeifa Adam Abd Alshafy; Mohamed Elhafiz Mustafa 2014 14th International Conference on Intelligent Systems Design and Applications, 2014

This paper presents a novel system approach for online Arabic handwriting recognition. The approach segments the word using new character boundaries detection based algorithm. Moreover it employs HMM-based classification method for the recognition. A dataset: Sudan University of Science and Technology Online Arabic Handwriting (SUSTOLAH) is used in testing the proposed approach. Promising experimental results of testing the approach with ...


More eLearning Resources

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IEEE-USA E-Books

  • Recognizing Textual Entailment:Models and Applications

    In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research community. This level of abstraction also facilitates evaluation, a crucial component of any technological advancement program. This book explains the RTE task formulation adopted by the NLP research community, and gives a clear overview of research in this area. It draws out commonalities in this res arch, detailing the intuitions behind dominant approaches and their theoretical underpinnings. This book has been written with a wide audience in mind, but is intended to inform all readers about the state of the art in this fascinating field, to give a clear understanding of the principles underlying RTE research to date, and to highlight the short- and long-term research goals that will advance this technology.

  • Latent Semantic Mapping:Principles and Applications

    Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring "noise." This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimens onality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications / Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Ve ification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography

  • Computational Modeling of Narrative

    The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narrati es and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticist ), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions

  • Learning Algorithms

    Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC- Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

  • Appendix: List of Symbols and Abbreviations

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • Representation Discovery using Harmonic Analysis

    Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infin te-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Tab e of Contents: Overview / Vector Spaces / Fourier Bases on Graphs / Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study: State-Space Planning / Case Study: Computer Graphics / Case Study: Natural Language / Future Directions

  • Appendices

    Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC- Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

  • The Meaning of In-Depth Understanding

    This chapter contains sections titled: BORIS -- A Computer Program, What BORIS Is Up Against, Knowledge and Memory for Comprehension, Natural Language Processing: Some Background, In-Depth Understanding, Methodology, Scope, and Aims, A Guide to the Reader

  • Bibliography

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • Domain-Sensitive Temporal Tagging

    <p>This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format. It places a special focus on the challenges and opportunities of domain- sensitive temporal tagging. After providing background knowledge on the concept of time, the book continues with a comprehensive survey of current research on temporal tagging. The authors provide an overview of existing techniques and tools, and highlight key issues that need to be addressed. This book is a valuable resource for researchers and application developers who need to become familiar with the topic and want to know the recent trends, current tools and techniques, as well as different application domains in which temporal information is of utmost importance. </p><p> Due to the prevalence of temporal expressions in diverse types of documents and the importance of temporal information in any information space, temporal tagging is an important task in natural language processing (NLP), and applications of several domains can benefit from the output of temporal taggers to provide more meaningful and useful results. </p><p> In recent years, temporal tagging has been an active field in NLP and computational linguistics. Several approaches to temporal tagging have been proposed, annotation standards have been developed, gold standard data sets have been created, and research competitions have been organized. Furthermore, some temporal taggers have also been made publicly available so that temporal tagging output is not just exploited in research, but is finding its way into real world applications. In addition, this book particularly focuses on domain-specific temporal tagging of documents. This is a crucial aspect as different types of documents (e.g., news articles, narratives, and colloquial texts) result in diverse challenges for temporal taggers and should be processed in a domain-sensitive man er.</p>



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