JOINTSTRUCTUREDPRED

Machine Learning Methods for Complex Outputs and Their Application to Natural Language Processing and Computational Biology

Coordinatore	MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.    more  Organization address address: Hofgartenstrasse 8 city: MUENCHEN postcode: 80539 contact info Titolo: Mr. Nome: Patrice Cognome: Wegener Email: send email Telefono: -9414 Fax: -9416
Nazionalità Coordinatore	Germany [DE]
Totale costo	153˙931 €
EC contributo	153˙931 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2007-2-1-IEF
Funding Scheme	MC-IEF
Anno di inizio	2010
Periodo (anno-mese-giorno)	2010-01-01   -   2011-01-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.  Organization address address: Hofgartenstrasse 8 city: MUENCHEN postcode: 80539 contact info Titolo: Mr. Nome: Patrice Cognome: Wegener Email: send email Telefono: -9414 Fax: -9416	DE (MUENCHEN)	coordinator	0.00

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 Obiettivo del progetto (Objective)

'In this project, we are interested in developing machine learning methods for complex inference problems that occur frequently in real world applications. Such problems are ubiquitous in many fields, ranging from natural language processing to bioinformatics, from computer vision to information retrieval. Examples include automatic translation of documents across languages, motion tracking of individuals in video sequences and identifying 3D structure of proteins. The predominant approach for such problems is to define simpler subtasks, to solve these subtasks in a cascaded manner and to use the output of the subtasks as input for the target task. This approach suffers from error propagation along the cascaded processes. Moreover, it does not take the correlation among the tasks into account, which might be a valuable source to improve the performance of each task. We propose a principled machine learning method for complex inference problems which overcomes the limitations of the cascaded approach and takes a unified approach in modeling the target task and the subtasks. Based on the assumption that the correlated tasks on an input space should have similar smoothness properties, we propose a novel and efficient learning method that performs optimization of the multiple tasks respecting the proposed model. We propose applying this method to various applications in natural language processing and computational biology. This project has the potential to contribute towards technological advances in a large spectrum of applications.'

Introduzione (Teaser)

A novel approach to machine learning with improved prediction accuracy for complex tasks has been developed. It has the potential to contribute towards technological advances in a large spectrum of applications.

Descrizione progetto (Article)

Machine learning is the study of computer programmes that allow computers (or machines) to learn to do things and to improve their own performance. It is an artificial intelligence approach that is widely used to classify data records into discrete categories or labels. However, in many tasks, some labelling decisions are inter-related and have to be decided simultaneously. Such complex tasks are called structured predictions.

There are problems such as error propagation with the current method of dealing with these complex tasks. The goal of the EU-funded Jointstructuredpred project was to overcome these limitations. The project proposed a method for dealing with complex tasks that involved training the subtasks jointly using multi-task learning techniques. To compare this new approach and the current one, a software programme was developed for both methods for structured prediction.

An evaluation of both methods using typical problems in computational biology showed that the proposed method outperformed the current method by significantly improving the prediction accuracy. The findings were similar, but not as pronounced, when they were compared using typical applications in natural language processing. Natural language processing is the computerised approach to processing human language in terms of its meaning while computational biology combines computer science and molecular biology.

Project results indicate that the proposed approach can be used to improve the performance of many prediction problems in a wide range of disciplines, including biology, medicine, linguistics and signal processing.