Conferences related to Natural Language Processing

Back to Top

ICASSP 2017 - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2014 IEEE International Conference on Robotics and Automation (ICRA)

Robotics and Automation


2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2013)

Humanoids in the Real World: All related areas of humanoid robotics including locomotion, architectures, mechatronics, control, perception, planning, learning, neuroscience and interaction.

  • 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

    The conference theme is 'Humanoids and Humans: Towards A New Frontier.' More than a decade has passed since the first Humanoids conference. Over that period, science and technology have advanced significantly. It is time to revisit the original conception of humanoids -- human-like robots -- and engage the next stage of humanoid research. What can we do with the current and emerging research across broad areas of science and technology to explore the next generation of humanoids and their new relationship to humans? Papers contributing to answering this question from any aspects are solicited.

  • 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)

    The creation of general-purpose service and companion humanoid robots is one of the greatest challenges in today s robotics research with a potentially huge impact. Papers are solicited in all related areas of humanoid robotics including mechatronics, control, perception, planning, learning, neuroscience, and human-robot interaction.

  • 2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)

    Humanoid Robotics is an increasing research topic stimulated both by the perspective of highly challenging applications in servicing robotics and by renewing fundamental research topics in Robotics at large such as Mechatronics, Control, Decision Making and Human-Robot Interaction. More than that Humanoid Robotics opens synergetic researches towards Life and Human Science. Such openness will constitute the special theme of Humanoids2010.

  • 2009 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2009)

    1. Design and control of humanoid robots 2. Motion planning 3. Cognition, perception and learning for humanoid robots 4. Manipulation by humanoid robots 5. Humanoid robot platforms for applications 6. Stability and dynamics for humanoid robots 7. Software and hardware architecture and system integration 8. Human-humanoid interaction 9. Planning, localization and navigation 10. Human body and behavior modeling 11. Neuro-robotics and humanoids

  • 2008 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2008)

    1. Design and control of full-body 2. humanoid robots 3. Motion planning 4. Cognition, perception and learning for humanoid robots 5. Advanced components for humanoid robots 6. Sub-parts, e.g. hands, arms, legs and etc., for humanoid robots 7. Humanoid robot platforms for applications 8. Anthropomorphism in humanoid robotics 9. Software and hardware architecture and system integration 10. Human-humanoid interaction 11. Planning, localization and navigation 12. Development tools for hum


2013 Eighth International Conference on Digital Information Management (ICDIM)

The principal aim of this conference is to bring people in academia, research laboratories and industry together, and offer a collaborative platform to address the emerging issues and solutions in digital information science and technology. The ICDIM intends to bridge the gap between different areas of digital information management, science and technology.

  • 2012 Seventh International Conference on Digital Information Management (ICDIM)

    The principal aim of this conference is to bring people in academia, research laboratories and industry together, and offer a collaborative platform to address the emerging issues and solutions in digital information science and technology.

  • 2011 Sixth International Conference on Digital Information Management (ICDIM)

    The ICDIM 2011 is a forum of academic and industrial researchers and scientists in digital information management and technology. It addresses the research in significant areas of information management, database management, and process management.

  • 2010 Fifth International Conference on Digital Information Management (ICDIM)

    The International Conference on Digital Information Management is a multidisciplinary conference on digital information management, science and technology.

  • 2009 Fourth International Conference on Digital Information Management (ICDIM)

    he principal aim of this conference is to bring people in academia, research laboratories and industry and offer a collaborative platform to address the emerging issues and solutions in digital information science and technology. The ICDIM intends to bridge the gap between different areas of digital information management, science and technology. This forum will address a large number of themes and issues. The conference will have original research and industrial papers on the theory, design and implementat

  • 2008 Third International Conference on Digital Information Management (ICDIM)

    The International Conference on Digital Information Management is a multidisciplinary conference on digital information management, science and technology. The principal aim of this conference is to bring people in academia, research laboratories and industry and offer a collaborative platform to address the emerging issues and solutions in digital information science and technology. The ICDIM intends to bridge the gap between different areas of digital information management, science and technology. This f


2013 International Conference on Asian Language Processing (IALP)

The International Conference on Asian Language Processing (IALP) is a series of conferences with unique focus on Asian Language Processing. The conference aims to advance the science and technology of all the aspects of Asian Language Processing by providing a forum for researchers in the different fields of language study all over the world to meet.

  • 2012 International Conference on Asian Language Processing (IALP)

    The topics of the conference cover all aspects of natural language processing with a focus on Asian languages.

  • 2011 International Conference on Asian Language Processing (IALP)

    The International Conference on Asian Language Processing (IALP) is a conference series with a unique focus on Asian Language Processing. The conference aims to advance the science and technology of all the aspects of Asian Language Processing by providing a forum for researchers in the different fields of language study to meet.

  • 2010 International Conference on Asian Language Processing (IALP)

    The conference is a series of conferences with unique focus on Asian Language Processing. The conference aims to advance the science and technology of all the aspects of Asian Language Processing.

  • 2009 International Conference on Asian Language Processing (IALP 2009)

    The International Conference on Asian Language Processing (IALP) is a series of conference with unique focus on Asian Language Processing. The conference aims to advance the science and technology of all the aspect of Asian Language Processing by providing a forum for researchers in different fields of language study all over the world to meet.


More Conferences

Periodicals related to Natural Language Processing

Back to Top

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 ...


Proceedings of the IEEE

The most highly-cited general interest journal in electrical engineering and computer science, the Proceedings is the best way to stay informed on an exemplary range of topics. This journal also holds the distinction of having the longest useful archival life of any EE or computer related journal in the world! Since 1913, the Proceedings of the IEEE has been the ...


Signal Processing Magazine, IEEE

IEEE Signal Processing Magazine is ranked as the number three most-cited journal in Electrical and Electronics Engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This publication features tutorial style papers on signal processing research and applications. The primary means of communication of the society leadership ...



Most published Xplore authors for Natural Language Processing

Back to Top

Xplore Articles related to Natural Language Processing

Back to Top

Speech and text query based Tamil - English Cross Language Information Retrieval system

P. Iswarya; V. Radha 2014 International Conference on Computer Communication and Informatics, 2014

The number of Web Users accessing the Internet becomes increasing day by day. Any kind of required information can be obtained anytime by anybody from the web. Information retrieval is the fact that there is vast amount of garbage that surrounds any useful information. Such information should be easily accessible and digestible. Internet is no longer monolingual and non-English content ...


Making the VoiceWeb smarter - integrating intelligent component technologies and VoiceXML

M. Mittendorfer; G. Niklfeld; W. Winiwarter Proceedings of the Second International Conference on Web Information Systems Engineering, 2001

VoiceXML offers the prospect of a streamlined deployment process of voice interfaces for commercial applications, similar to the ease of Web development. This in turn promises increased dynamism in the field of speech and language research. We investigate opportunities and constraints for the integration of intelligent component technologies into VoiceXML-based systems: such components will solve tasks from both sides of ...


MSATS: Multilingual sentiment analysis via text summarization

Rupal Bhargava; Yashvardhan Sharma 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 2017

Sentiment Analysis has been a keen research area for past few years. Though much of the exploration that has been done supports English language only. This paper proposes a method using which one can analyze different languages to find sentiments in them and perform sentiment analysis. The method leverages different techniques of machine learning to analyze the text. Machine translation ...


Creating Indonesian-Javanese parallel corpora using wikipedia articles

Bayu Distiawan Trisedya; Dyah Inastra 2014 International Conference on Advanced Computer Science and Information System, 2014

Parallel corpora are necessary for multilingual researches especially in information retrieval (IR) and natural language processing (NLP). However, such corpora are hard to find, specifically for low-resources languages like ethnic languages. Parallel corpora of ethnic languages were usually collected manually. On the other hand, Wikipedia as a free online encyclopedia is supporting more and more languages each year, including ethnic ...


Spoken language mismatch in speaker verification: An investigation with NIST-SRE and CRSS Bi-Ling corpora

Abhinav Misra; John H. L. Hansen 2014 IEEE Spoken Language Technology Workshop (SLT), 2014

Compensation for mismatch between acoustic conditions in automatic speaker recognition has been widely addressed in recent years. However, performance degradation due to language mismatch has yet to be thoroughly addressed. In this study, we address langauge mismatch for speaker verification. We select bilingual speaker data from the NIST SRE 04-08 corpora and develop train/test- trials for language matched and mismatched ...


More Xplore Articles

Educational Resources on Natural Language Processing

Back to Top

eLearning

Speech and text query based Tamil - English Cross Language Information Retrieval system

P. Iswarya; V. Radha 2014 International Conference on Computer Communication and Informatics, 2014

The number of Web Users accessing the Internet becomes increasing day by day. Any kind of required information can be obtained anytime by anybody from the web. Information retrieval is the fact that there is vast amount of garbage that surrounds any useful information. Such information should be easily accessible and digestible. Internet is no longer monolingual and non-English content ...


Making the VoiceWeb smarter - integrating intelligent component technologies and VoiceXML

M. Mittendorfer; G. Niklfeld; W. Winiwarter Proceedings of the Second International Conference on Web Information Systems Engineering, 2001

VoiceXML offers the prospect of a streamlined deployment process of voice interfaces for commercial applications, similar to the ease of Web development. This in turn promises increased dynamism in the field of speech and language research. We investigate opportunities and constraints for the integration of intelligent component technologies into VoiceXML-based systems: such components will solve tasks from both sides of ...


MSATS: Multilingual sentiment analysis via text summarization

Rupal Bhargava; Yashvardhan Sharma 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 2017

Sentiment Analysis has been a keen research area for past few years. Though much of the exploration that has been done supports English language only. This paper proposes a method using which one can analyze different languages to find sentiments in them and perform sentiment analysis. The method leverages different techniques of machine learning to analyze the text. Machine translation ...


Creating Indonesian-Javanese parallel corpora using wikipedia articles

Bayu Distiawan Trisedya; Dyah Inastra 2014 International Conference on Advanced Computer Science and Information System, 2014

Parallel corpora are necessary for multilingual researches especially in information retrieval (IR) and natural language processing (NLP). However, such corpora are hard to find, specifically for low-resources languages like ethnic languages. Parallel corpora of ethnic languages were usually collected manually. On the other hand, Wikipedia as a free online encyclopedia is supporting more and more languages each year, including ethnic ...


Spoken language mismatch in speaker verification: An investigation with NIST-SRE and CRSS Bi-Ling corpora

Abhinav Misra; John H. L. Hansen 2014 IEEE Spoken Language Technology Workshop (SLT), 2014

Compensation for mismatch between acoustic conditions in automatic speaker recognition has been widely addressed in recent years. However, performance degradation due to language mismatch has yet to be thoroughly addressed. In this study, we address langauge mismatch for speaker verification. We select bilingual speaker data from the NIST SRE 04-08 corpora and develop train/test- trials for language matched and mismatched ...


More eLearning Resources

IEEE.tv Videos

Computing with Words: Towards an Ultimately Human Centric Computing Paradigm
ICASSP 2010 - New Signal Processing Application Areas
Risto Miikkilainen - Multiagent Learning Through Neuroevolution
Computer-Assisted Audiovisual Language Learning
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
Neural Processor Design Enabled by Memristor Technology - Hai Li: 2016 International Conference on Rebooting Computing
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Computing Conversations: Bertrand Meyer: Eiffel Programming Language
Inventor C++ Bjarne Stroustrup (high resolution)
Deep Learning and the Representation of Natural Data
Erasing Logic-Memory Boundaries in Superconductor Electronics - Vasili Semenov: 2016 International Conference on Rebooting Computing
TRIC VI @ Argentina
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 6 of 7 - A high sensitive magnetometer system for natural magnetic field measurements
Superconductors for the Future from the Perspective of the Past
ICASSP 2011 Trends in Machine Learning for Signal Processing
Computing Conversations with Brendan Eich
The AcceleGlove: A Cheap and Lightweight Control Glove
Introduction to Chip Multiprocessor Architecture
Hamid R Tizhoosh - Fuzzy Image Processing
ICASSP 2011 Trends in Design and Implementation of Signal Processing Systems

IEEE-USA E-Books

  • No title

    This book introduces Chinese language-processing issues and techniques to readers who already have a basic background in natural language processing (NLP). Since the major difference between Chinese and Western languages is at the word level, the book primarily focuses on Chinese morphological analysis and introduces the concept, structure, and interword semantics of Chinese words. The following topics are covered: a general introduction to Chinese NLP; Chinese characters, morphemes, and words and the characteristics of Chinese words that have to be considered in NLP applications; Chinese word segmentation; unknown word detection; word meaning and Chinese linguistic resources; interword semantics based on word collocation and NLP techniques for collocation extraction. Table of Contents: Introduction / Words in Chinese / Challenges in Chinese Morphological Processing / Chinese Word Segmentation / Unknown Word Identification / Word Meaning / Chinese Collocations / Automatic Chinese Coll cation Extraction / Appendix / References / Author Biographies

  • No title

    Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. R nking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, col aborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

  • No title

    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

  • Index

    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.

  • No title

    Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a high r syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better under tanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented

  • Query Translation Using Evolutionary Programming for Multi-Lingual Information Retrieval

    Multi-lingual information retrieval (IR) systems apply queries in one language to a document collection in several different languages with the goal of retrieving only those documents relevant to the query. At first glance, deep linguistic analysis and translation of the query appears necessary before retrievals can be performed. IR systems are unique in natural language processing, however, because a pattern of term occurrences in a document generally suffices to determine the subject matter; word order is largely irrelevant. Translated queries are therefore primarily derived by a mapping from a word set in the query language to a word set in the language of the derived query. Large parallel text collections with sentencelevel alignments can provide a baseline for evaluating the correctness of a query translation, but the determination of members of the query translation remains problematic. Constructing a query from machine-readable, bilingual dictionaries and assigning term weights by the evolutionary optimization of a population of potential weighting schemes presents a solution to the difficulties of generating translated queries. In this approach, differences in the rank statistics on the comparative recall results for a query against its native language and its translation against its native language determine the fitness of a tentative query translation.

  • No title

    This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. The goal is to introduce Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. The book discusses Arabic script, phonology, orthography, morphology, syntax and semantics, with a final chapter on machine translation issues. The chapter sizes correspond more or less to what is linguistically distinctive about Arabic, with morphology getting the lion's share, followed by Arabic script. No previous knowledge of Arabic is needed. This book is designed for computer scientists and linguists alike. The focus of the book is on Modern Standard Arabic; however, notes on practical issues related to Arabic dialects and languages written in the Arabic script are presented in different chapters. Table of Contents: What is "Arabic"? / Arabic Script / Arabic Phonology and Orthograph / Arabic Morphology / Computational Morphology Tasks / Arabic Syntax / A Note on Arabic Semantics / A Note on Arabic and Machine Translation

  • 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

  • Index

    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.

  • Languages and Ontologies

    As the World Wide Web continues to expand, it becomes increasingly difficult for users to obtain information efficiently. Because most search engines read format languages such as HTML or SGML, search results reflect formatting tags more than actual page content, which is expressed in natural language. Spinning the Semantic Web describes an exciting new type of hierarchy and standardization that will replace the current "web of links" with a "web of meaning." Using a flexible set of languages and tools, the Semantic Web will make all available information -- display elements, metadata, services, images, and especially content -- accessible. The result will be an immense repository of information accessible for a wide range of new applications.This first handbook for the Semantic Web covers, among other topics, software agents that can negotiate and collect information, markup languages that can tag many more types of information in a document, and knowledge systems that enable machines to read Web pages and determine their reliability. The truly interdisciplinary Semantic Web combines aspects of artificial intelligence, markup languages, natural language processing, information retrieval, knowledge representation, intelligent agents, and databases.



Standards related to Natural Language Processing

Back to Top

No standards are currently tagged "Natural Language Processing"