TAILIEUCHUNG - Báo cáo khoa học: "a Toolkit for Distributed Perceptron Training and Prediction with MapReduce"

We propose a set of open-source software modules to perform structured Perceptron Training, Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing, the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties, thus guaranties same accuracy performances as the serial Perceptron. . | HadoopPerceptron a Toolkit for Distributed Perceptron Training and Prediction with MapReduce Andrea Gesmundo Computer Science Department University of Geneva Geneva Switzerland Nadi Tomeh LIMSI-CNRS and Universite Paris-Sud Orsay France Abstract We propose a set of open-source software modules to perform structured Perceptron Training Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties thus guaranties same accuracy performances as the serial Perceptron. The presented modules can be executed as stand-alone software or easily extended or integrated in complex systems. The execution of the modules applied to specific NLP tasks can be demonstrated and tested via an interactive web interface that allows the user to inspect the status and structure of the cluster and interact with the MapReduce jobs. 1 Introduction The Perceptron training algorithm Rosenblatt 1958 Freund and Schapire 1999 Collins 2002 is widely applied in the Natural Language Processing community for learning complex structured models. The non-probabilistic nature of the perceptron parameters makes it possible to incorporate arbitrary features without the need to calculate a partition function which is required for its discriminative probabilistic counterparts such as CRFs Lafferty et al. 2001 . Additionally the Perceptron is robust to approximate inference in large search spaces. Nevertheless Perceptron training is proportional to inference which is frequently non-linear in the input sequence size. Therefore training can be time-consuming for complex model structures. Furthermore for an .

• Báo cáo khoa học
• a Toolkit for Distributed Perceptron Training
• Prediction with MapReduce
• Andrea Gesmundo
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln