Arlindo L. Oliveira (ed.)

Grammatical Inference: Algorithms and Applications

5th International Colloquium, ICGI 2000, Lisbon, Portugal, September 11-13, 2000 Proceedings
Herausgegeben:Oliveira, Arlindo L.

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Produktbeschreibung

The Fifth International Colloquium on Grammatical Inference (ICGI-2000) was heldinLisbononSeptember11 13th,2000.ICGI-2000wasthe?fthinaseriesof successfulbiennialinternationalconferencesintheareaofgrammaticalinference. Previous conferences were held in Essex, U.K.; Alicante, Spain; Montpellier, France; and Ames, Iowa, USA. This series of meetings seeks to provide a forum for the presentation and discussion of original research on all aspects of grammatical inference. Gram- tical inference, the process of inferring grammar from given data, is a ?eld that is not only challenging from a purely scienti?c standpoint but also ?nds many applications in real world problems. Despitethefactthatgrammaticalinferenceaddressesproblemsinarelatively narrow area, it uses techniques from many domains, and intersects a number of di?erent disciplines. Researchers in grammatical inference come from ?elds as diverse as machine learning, theoretical computer science, computational ling- stics, pattern recognition and arti?cial neural networks. From a practical standpoint, applications in areas such as natural language acquisition, computational biology, structural pattern recognition, information retrieval, text processing and adaptive intelligent agents have either been - monstrated or proposed in the literature. ICGI-2000 was held jointly with CoNLL-2000, the Computational Natural Language Learning Workshop and LLL-2000, the Second Learning Language in LogicWorkshop.Thetechnicalprogramincludedthepresentationof24accepted papers (out of 35 submitted) as well as joint sessions with CoNLL and LLL. A tutorial program organized by Gabriel Pereira Lopes took place after the meetings and included tutorials by Raymond Mooney, Gregory Grefenstette, Walter Daelemans, Ant onio Ribeiro, Joaquim Ferreira da Silva, Gael Dias, Nuno Marques,VitorRossio,Jo aoBalsaandAlexandreAgostini.Thejointrealization of these events represents a unique opportunity for researchers in these related ?elds to interact and exchange ideas.

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Produktdetails

• Lecture Notes in Computer Science 1891
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-41011-9
• 2000
• Seitenzahl: 324
• Erscheinungstermin: 1. September 2000
• Englisch
• Abmessung: 235mm x 155mm x 18mm
• Gewicht: 463g
• ISBN-13: 9783540410119
• ISBN-10: 3540410112
• Artikelnr.: 23295114

Inhaltsangabe

Inference of Finite-State Transducers by Using Regular Grammars and Morphisms.- Computational Complexity of Problems on Probabilistic Grammars and Transducers.- Efficient Ambiguity Detection in C-NFA.- Learning Regular Languages Using Non Deterministic Finite Automata.- Smoothing Probabilistic Automata: An Error-Correcting Approach.- Inferring Subclasses of Contextual Languages.- Permutations and Control Sets for Learning Non-regular Language Families.- On the Complexity of Consistent Identification of Some Classes of Structure Languages.- Computation of Substring Probabilities in Stochastic Grammars.- A Comparative Study of Two Algorithms for Automata Identification.- The Induction of Temporal Grammatical Rules from Multivariate Time Series.- Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata.- Iterated Transductions and Efficient Learning from Positive Data: A Unifying View.- An Inverse Limit of Context-Free Grammars - A New Approach to Identifiability in the Limit.- Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm.- Combination of Estimation Algorithms and Grammatical Inference Techniques to Learn Stochastic Context-Free Grammars.- On the Relationship between Models for Learning in Helpful Environments.- Probabilistic k-Testable Tree Languages.- Learning Context-Free Grammars from Partially Structured Examples.- Identification of Tree Translation Rules from Examples.- Counting Extensional Differences in BC-Learning.- Constructive Learning of Context-Free Languages with a Subpansive Tree.- A Polynomial Time Learning Algorithm of Simple Deterministic Languages via Membership Queries and a Representative Sample.- Improve the Learning of Subsequential Transducers by Using Alignments andDictionaries.