Summary of the paper

Title Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
Authors Guillaume Jacquet, Maud Ehrmann, Ralf Steinberger and Jaakko Väyrynen
Abstract This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource. Starting from a collection of millions of acronym/expansion pairs for 22 languages where expansion variants were grouped into monolingual clusters, we experiment with several aggregation strategies to link these clusters across languages. Aggregation strategies make use of string similarity distances and translation probabilities and they are based on vector space and graph representations. The accuracy of the approach is evaluated against Wikipedia's redirection and cross-lingual linking tables. The resulting multi-word entity resource contains 64,000 multi-word entities with unique identifiers and their 600,000 multilingual lexical variants. We intend to make this new resource publicly available.
Topics MultiWord Expressions & Collocations, Multilinguality, Named Entity Recognition
Full paper Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
Bibtex @InProceedings{JACQUET16.428,
  author = {Guillaume Jacquet and Maud Ehrmann and Ralf Steinberger and Jaakko Väyrynen},
  title = {Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
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