The Experts below are selected from a list of 39711 Experts worldwide ranked by ideXlab platform
Dan Mandeville - One of the best experts on this subject based on the ideXlab platform.
-
Library Guides: Computational Linguistics: Find Books & Ebooks
2009Co-Authors: Dan MandevilleAbstract:Guide to reference sources and databases in the field of Computational Linguistics Use UW Libraries Search to find Computational Linguistics books and ebooks.
-
Library Guides: Computational Linguistics: BibTeX
2009Co-Authors: Dan MandevilleAbstract:Guide to reference sources and databases in the field of Computational Linguistics How to generate BibTEX citations with Refworks and Databases
-
Library Guides: Computational Linguistics: Articles & Conference Papers
2009Co-Authors: Dan MandevilleAbstract:Guide to reference sources and databases in the field of Computational Linguistics Suggested databases for articles and conference papers
-
Library Guides: Computational Linguistics: Find Journal Articles
2009Co-Authors: Dan MandevilleAbstract:Guide to reference sources and databases in the field of Computational Linguistics Suggested databases for articles and conference papers
Aravind K. Joshi - One of the best experts on this subject based on the ideXlab platform.
-
Proceedings of the 23rd International Conference on Computational Linguistics: Posters
2010Co-Authors: Aravind K. Joshi, Chu-ren Huang, Dan JurafskyAbstract:You will find in this volume papers from the 23rd International Conference on Computational Linguistics (COLING 2010) held in Beijing, China on August 23-27, 2010 under the auspices of the International Committee on Computational Linguistics (ICCL), and organized by the Chinese Information Processing Society (CIPS) of China. For this prestigious natural language processing conference to be held in China is a significant event for Computational Linguistics and for colleagues in China, demonstrating both the maturity of our field and the development of academic areas in China.
-
Proceedings of the 23rd International Conference on Computational Linguistics
2010Co-Authors: Aravind K. Joshi, Chu-ren Huang, Dan JurafskyAbstract:You will find in this volume papers from the 23rd International Conference on Computational Linguistics (COLING 2010) held in Beijing, China on August 23-27, 2010 under the auspices of the International Committee on Computational Linguistics (ICCL), and organized by the Chinese Information Processing Society (CIPS) of China. For this prestigious natural language processing conference to be held in China is a significant event for Computational Linguistics and for colleagues in China, demonstrating both the maturity of our field and the development of academic areas in China. COLING started as a friendly gathering in New York in 1965, and has grown steadily since. Yet COLINGs aspiration to be a different conference remains the same. COLING strives to maintain its key qualities of embracing different theories and encouraging young scholars in spite of its growing size. A new component introduced at COLING 2010 underlines this quality. A RefreshINGenious (RING) session, organized by Aravind Joshi, our General Chair, allows new and un-orthodox ideas to be presented before they are fully developed in order to generate more discussion and stimulate other new ideas. We hope that this can become an important feature of COLING in the future. The 155 oral papers included in the hardcopy proceedings published by Tsinghua University Press, as well as the 334 papers included in the electronic proceedings (the same 155 oral papers plus 179 poster papers) are selected from among 815 effective submissions among the more than 840 submissions received. The very selective acceptance rate of 19.02% for oral presentations (155/815 submissions) indicates the extremely high quality of the papers. An additional 21.96% (179/815) are selected for poster presentations to bring the overall acceptance rate to 40.98% (334/815).
-
Computational Linguistics: A new tool for exploring biopolymer structures and statistical mechanics
Polymer, 2007Co-Authors: Ken A. Dill, Adam R. Lucas, Julia Hockenmaier, Liang Huang, David Chiang, Aravind K. JoshiAbstract:Unlike homopolymers, biopolymers are composed of specific sequences of different types of monomers. In proteins and RNA molecules, one-dimensional sequence information encodes a three-dimensional fold, leading to a corresponding molecular function. Such folded structures are not treated adequately through traditional methods of polymer statistical mechanics. A promising new way to solve problems of the statistical mechanics of biomolecules comes from Computational Linguistics, the field that uses computers to parse and understand the sentences in natural languages. Here, we give two examples. First, we show that a dynamic programming method of Computational Linguistics gives a fast way to search protein models for native structures. Interestingly, the Computational search process closely resembles the physical folding process. Second, Linguistics-based dynamic programming methods are also useful for computing partition functions and densities of states for some foldable biopolymers e helix-bundle proteins are reviewed here. In these ways, Computational Linguistics is helping to solve problems of the searching and counting of biopolymer conformations. 2007 Elsevier Ltd. All rights reserved.
Barbara Mcgillivray - One of the best experts on this subject based on the ideXlab platform.
-
Latin Computational Linguistics
Methods in Latin Computational Linguistics, 2014Co-Authors: Barbara McgillivrayAbstract:Computational Linguistics and quantitative corpus Linguistics offer valuable resources and techniques for doing what Latin linguists have always done. The crucial frequency information contained in the valency lexicon allows for further corpus-based synchronic and diachronic investigations on verbal argument structure which account for language usage. This chapter contributes to advances in Latin Linguistics and also aims at inspiring Computational linguists. The approach relies on the relations of synonymy and hyperonymy as defined in Latin WordNet, while the latter measures the similarity between two words in terms of the syntactic and lexical contexts. The chapter exploits existing collections of Latin texts, aiming at showing how new resources can be built from these collections and how they can be quantitatively analysed. The methodological contribution in the chapter is intrinsically cross-disciplinary, because it defines Latin Computational Linguistics from the combination of Computational methods and Latin data.Keywords: corpus-based synchronic and diachronic investigations; Latin Computational Linguistics; Latin valencylexicons; Latin WordNet; quantitative corpus Linguistics
-
Methods in Latin Computational Linguistics
2013Co-Authors: Barbara McgillivrayAbstract:Historical languages, corpora, and Computational methods -- Computational resources and tools for Latin -- Verbs in corpora, lexicon ex machina -- The agonies of choice: automatic selectional preferences -- A closer look at automatic selectional preferences for Latin -- A corpus-based foray into Latin preverbs -- Statistical background to the investigation on preverbs -- Latin Computational Linguistics.
Michael Witt - One of the best experts on this subject based on the ideXlab platform.
-
Library Guides: Computational Linguistics: Journals
2016Co-Authors: Michael WittAbstract:A guide to library and information resources in Computational Linguistics A list of top journals with papers on topics exploring Computational Linguistics
-
Library Guides: Computational Linguistics: Linguistics Data Consortium
2016Co-Authors: Michael WittAbstract:A guide to library and information resources in Computational Linguistics LDC publishes a regular catalog of datasets used in Computational Linguistics research
Bhargav Srinivasa-desikan - One of the best experts on this subject based on the ideXlab platform.
-
Natural Language Processing and Computational Linguistics
2018Co-Authors: Bhargav Srinivasa-desikanAbstract:Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and Computational Linguistics algorithms.About This BookDiscover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and KerasHands-on text analysis with Python, featuring natural language processing and Computational Linguistics algorithmsLearn deep learning techniques for text analysisWho This Book Is ForThis book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!What You Will LearnWhy text analysis is important in our modern ageUnderstand NLP terminology and get to know the Python tools and datasetsLearn how to pre-process and clean textual dataConvert textual data into vector space representationsUsing spaCy to process textTrain your own NLP models for Computational LinguisticsUse statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learnEmploy deep learning techniques for text analysis using KerasIn DetailModern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and Computational Linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform Computational Linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and Computational Linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.Style and approachThe book teaches NLP from the angle of a practitioner as well as that of a student. This is a tad unusual, but given the enormous speed at which new algorithms and approaches travel from scientific beginnings to industrial implementation, first principles can be clarified with the help of entirely practical examples.
-
Natural Language Processing and Computational Linguistics
2018Co-Authors: Bhargav Srinivasa-desikanAbstract:Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and Computational Linguistics algorithms.About This BookDiscover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and KerasHands-on text analysis with Python, featuring natural language processing and Computational Linguistics algorithmsLearn deep learning techniques for text analysisWho This Book Is ForThis book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!What You Will LearnWhy text analysis is important in our modern ageUnderstand NLP terminology and get to know the Python tools and datasetsLearn how to pre-process and clean textual dataConvert textual data into vector space representationsUsing spaCy to process textTrain your own NLP models for Computational LinguisticsUse statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learnEmploy deep learning techniques for text analysis using KerasIn DetailModern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and Computational Linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform Computational Linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and Computational Linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.Style and approachThe book teaches NLP from the angle of a practitioner as well as that of a student. This is a tad unusual, but given the enormous speed at which new algorithms and approaches travel from scientific beginnings to industrial implementation, first principles can be clarified with the help of entirely practical examples.
