Finite State Methods And Natural Language Processing
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Finite-State Methods and Natural Language Processing
These proceedings contain the final versions of the papers presented at the 7th International Workshop on Finite-State Methods and Natural Language Processing (FSMNLP), held in Ispra, Italy, on September 11–12, 2008. The aim of the FSMNLP workshops is to bring together members of the research and industrial community working on finite-state based models in language technology, computational linguistics, web mining, linguistics and cognitive science on one hand, and on related theory and methods in fields such as computer science and mathematics on the other. Thus, the workshop series is a forum for researchers and practitioners working on applications as well as theoretical and implementation aspects. The special theme of FSMNLP 2008 was high performance finite-state devices in large-scale natural language text processing systems and applications. The papers in this publication cover a range of interesting NLP applications, including machine learning and translation, logic, computational phonology, morphology and semantics, data mining, information extraction and disambiguation, as well as programming, optimization and compression of finite-state networks. The applied methods include weighted algorithms, kernels and tree automata. In addition, relevant aspects of software engineering, standardization and European funding programmes are discussed.
Finite-State Methods and Natural Language Processing
Author: Anssi Yli-Jyrä
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-12-07
This book constitutes the thoroughly refereed post-proceedings of the 5th International Workshop on Finite-State Methods in Natural Language Processing, FSMNLP 2005, held in Helsinki, Finland, September 2005. The book presents 24 revised full papers and seven revised poster papers together with two invited contributions and abstracts of six software demos. Topics include morphology, optimality theory, some special FSM families, weighted FSM algorithms, FSM representations, exploration, ordered structures, and surface parsing.
Formal Analysis for Natural Language Processing: A Handbook
The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is now rapidly evolving, as new methods and toolsets converge with an ever-expanding wealth of available data. This state-of-the-art handbook addresses all aspects of formal analysis for natural language processing. Following a review of the field’s history, it systematically introduces readers to the rule-based model, statistical model, neural network model, and pre-training model in natural language processing. At a time characterized by the steady and vigorous growth of natural language processing, this handbook provides a highly accessible introduction and much-needed reference guide to both the theory and method of NLP. It can be used for individual study, as the textbook for courses on natural language processing or computational linguistics, or as a supplement to courses on artificial intelligence, and offers a valuable asset for researchers, practitioners, lecturers, graduate and undergraduate students alike.