Anna Widiger


2006

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The JRC-Acquis: A Multilingual Aligned Parallel Corpus with 20+ Languages
Ralf Steinberger | Bruno Pouliquen | Anna Widiger | Camelia Ignat | Tomaž Erjavec | Dan Tufiş | Dániel Varga
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We present a new, unique and freely available parallel corpus containing European Union (EU) documents of mostly legal nature. It is available in all 20 official EU languages, with additional documents being available in the languages of the EU candidate countries. The corpus consists of almost 8,000 documents per language, with an average size of nearly 9 million words per language. Pair-wise paragraph alignment information produced by two different aligners (Vanilla and HunAlign) is available for all 190+ language pair combinations. Most texts have been manually classified according to the EUROVOC subject domains so that the collection can also be used to train and test multi-label classification algorithms and keyword-assignment software. The corpus is encoded in XML, according to the Text Encoding Initiative Guidelines. Due to the large number of parallel texts in many languages, the JRC-Acquis is particularly suitable to carry out all types of cross-language research, as well as to test and benchmark text analysis software across different languages (for instance for alignment, sentence splitting and term extraction).

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Geocoding Multilingual Texts: Recognition, Disambiguation and Visualisation
Bruno Pouliquen | Marco Kimler | Ralf Steinberger | Camelia Ignat | Tamara Oellinger | Ken Blackler | Flavio Fluart | Wajdi Zaghouani | Anna Widiger | Ann-Charlotte Forslund | Clive Best
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We are presenting a method to recognise geographical references in free text. Our tool must work on various languages with a minimum of language-dependent resources, except a gazetteer. The main difficulty is to disambiguate these place names by distinguishing places from persons and by selecting the most likely place out of a list of homographic place names world-wide. The system uses a number of language-independent clues and heuristics to disambiguate place name homographs. The final aim is to index texts with the countries and cities they mention and to automatically visualise this information on geographical maps using various tools.