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Large-scale, Cross-lingual Trend Mining and Summarisation of Real-time Media Streams

Large-scale, Cross-lingual Trend Mining and Summarisation of Real-time Media Streams

Objective

The recent massive growth in online media and the rise of user-authored content (e.g weblogs, Twitter, Facebook) has lead to challenges of how to access and interpret these strongly multilingual data, in a timely, efficient, and affordable manner. Scientifically, streaming online media pose new challenges, due to their shorter, noisier, and more colloquial nature. Moreover, they form a temporal stream strongly grounded in events and context. Consequently, existing language technologies fall short on accuracy, scalability and portability.The goal of this project is to deliver. innovative, portable open-source real-time methods for cross-lingual mining and summarisation of large-scale stream media.TrendMiner will achieve this through an inter-disciplinary approach, combining deep linguistic methods from text processing, knowledge-based reasoning from web science, machine learning, economics, and political science. No expensive human annotated data will be required due to our use of time-series data (e.g. financial markets, political polls) as a proxy. A key novelty will be weakly supervised machine learning algorithms for automatic discovery of new trends and correlations. Scalability and affordability will be addressed through a cloud-based infrastructure for real-time text mining from stream media.Results will be validated in two high-profile case studies: financial decision support (with analysts, traders, regulators, and economists) and political analysis and monitoring (with politicians, economists, and political journalists).The techniques will be generic with many business applications: business intelligence, customer relations management, community support. The project will also benefit society and ordinary citizens by enabling enhanced access to government data archives, summarisation of online health information , and tracking of hot societal issues.TrendMiner addresses Objective ICT-2011.4.2 Language Technologies, target outcome b) Information access and mining.

Coordinator

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Address

Trippstadter Strasse 122
67663 Kaiserslautern

Germany

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 557 870

Administrative Contact

Walter OLTHOFF (Dr.)

Participants (12)

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SORA OGRIS & HOFINGER GMBH

Austria

EU Contribution

€ 274 189

ONTOTEXT AD

Bulgaria

EU Contribution

€ 324 205

UNIVERSIDAD CARLOS III DE MADRID

Spain

EU Contribution

€ 93 753

DAEDALUS-DATA, DECISIONS AND LANGUAGE, S.A.

Spain

EU Contribution

€ 113 816

INTERNET MEMORY RESEARCH SAS

France

EU Contribution

€ 239 247

MAGYAR TUDOMANYOS AKADEMIA, NYELVTUDOMANYI INTEZET

Hungary

EU Contribution

€ 71 720

Hardik Fintrade Pvt Ltd.

India

EU Contribution

€ 35 520

EUROKLEIS SRL

Italy

EU Contribution

€ 203 000

STICHTING INTERNET MEMORY FOUNDATION

Netherlands

EU Contribution

€ 60 933

Instytut Podstaw Informatyki Polskiej Akademii Nauk

Poland

EU Contribution

€ 77 336

THE UNIVERSITY OF SHEFFIELD

United Kingdom

EU Contribution

€ 485 461

UNIVERSITY OF SOUTHAMPTON

United Kingdom

EU Contribution

€ 348 950

Project information

Grant agreement ID: 287863

Status

Closed project

  • Start date

    1 November 2011

  • End date

    31 October 2014

Funded under:

FP7-ICT

  • Overall budget:

    € 3 683 928

  • EU contribution

    € 2 886 000

Coordinated by:

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Germany