Summary of the paper
| Title | A Language-independent and fully Unsupervised Approach to Lexicon Induction and Part-of-Speech Tagging for Closely Related Languages |
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| Authors | Yves Scherrer and Benoît Sagot |
| Abstract | In this paper, we describe our generic approach for transferring part-of-speech annotations from a resourced language towards an etymologically closely related non-resourced language, without using any bilingual (i.e., parallel) data. We first induce a translation lexicon from monolingual corpora, based on cognate detection followed by cross-lingual contextual similarity. Second, POS information is transferred from the resourced language along translation pairs to the non-resourced language and used for tagging the corpus. We evaluate our methods on three language families, consisting of five Romance languages, three Germanic languages and five Slavic languages. We obtain tagging accuracies of up to 91.6%. |
| Topics | Statistical and Machine Learning Methods, Corpus (Creation, Annotation, etc.) |
Full paper  |
A Language-independent and fully Unsupervised Approach to Lexicon Induction and Part-of-Speech Tagging for Closely Related Languages |
| Bibtex | @InProceedings{SCHERRER14.797, author = {Yves Scherrer and Benoît Sagot}, title = {A Language-independent and fully Unsupervised Approach to Lexicon Induction and Part-of-Speech Tagging for Closely Related Languages}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |