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Cross-lingual Knowledge Extraction Project Partners Deliverables Factsheet News Prototypes Publications The goal of the XLike project is to develop technology to monitor and aggregate knowledge that is currently spread across mainstream and social media, and to enable cross-lingual services for publishers, media monitoring and business intelligence. The aim is to combine scientific insights from several scientific areas to contribute in the area of cross-lingual text understanding. By combining modern computational linguistics, machine learning, text mining and semantic technologies we plan to deal with the following two key open research problems: to extract and integrate formal knowledge from multilingual texts with cross-lingual knowledge bases, and to adapt linguistic techniques and crowdsourcing to deal with irregularities in informal language used primarily in social media. The developed technology will be language-agnostic, while within the project we will specifically address English, German, Spanish, Chinese and Hindi as major world languages and Catalan and Slovenian as minority languages. FP7 XLike is funded by the European Community's Seventh Framework Programme FP7/2007-2013 Recent News Language Processing Pipeline Demo at NIPS 2013 TextToLogic: multilingual semi-automatic information extraction Event Registry Cross-lingual Semantic Annotation Links Language Processing Pipeline Open source prototypes from XLike GitHub code repository Open source prototypes from XLike Newsfeed Clean stream of semantically enriched news articles Multilingual Language Processing Wweb services for multilingual language processing Cross-lingual Document Linking Demo of cross-lingual similarity search News Data Visualization Interactive interface to Newsfeed data enriched with XLike technologies Event Registry QMiner Analytic platform for real-time large-scale streams containing structured and unstructured data Project related videos xLiTe: Cross-Lingual Technologies Kickoff Meeting 2012, Bled Site Admin | Theme by Niyaz XLike Copyright © 2015 All Rights Reserved
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