Europe is a multilingual society, in which dozens of languages are spoken. The only op tion to enable and to benefit from multilingual ism is through Language Technologies (LT), i. e., Natural Language Processing and Speech Technologies. We describe the European Lan guage Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella plat form for the European LT landscape, includ ing research and industry, enabling all stake holders to upload, share and distribute their ser vices, products and resources. At the end of our EU project, which will establish a legal en tity in 2022, the ELG will provide access to ap prox. 1300 services for all European languages as well as thousands of data sets.

Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability.

This paper addresses an important problem in Example-Based Machine Translation (EBMT), namely how to measure similarity between a sentence fragment and a set of stored examples. A new method is proposed that measures similarity according to both surface structure and content. A second contribution is the use of clustering to make retrieval of the best matching example from the database more efficient. Results on a large number of test cases from the CELEX database are presented.

This paper addresses the alignment issue in the framework of exploitation of large bimultilingual corpora for translation purposes. A generic alignment scheme is proposed that can meet varying requirements of different applications. Depending on the level at which alignment is sought, appropriate surface linguistic information is invoked coupled with information about possible unit delimiters. Each text unit (sentence, clause or phrase) is represented by the sum of its content tags. The results are then fed into a dynamic programming framework that computes the optimum alignment of units. The proposed scheme has been tested at sentence level on parallel corpora of the CELEX database. The success rate exceeded 99%. The next steps of the work concern the testing of the scheme's efficiency at lower levels endowed with necessary bilingual information about potential delimiters.

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