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This paper describes the test of a dependency parsing method which is based on bidirectional LSTM feature representations and multilingual word embedding, and evaluates the results on mono-and multilingual data. The results are similar in all cases, with a slightly better results achieved using multilingual data. The languages under investigation are Komi-Zyrian and Russian. Examination of the results by relation type shows that some language specific constructions are correctly recognized even when they appear in naturally occurring code-switching data. Tiivistelma Tutkimus arvioi dependenssianalyysin menetelmaa, joka perustuu kaksisuun-taiseen LSTM-piirrerepresentaatioon ja monikieliseen 'word embedding'-malliin, seka arvioi tuloksia yksi-ja monikielisissa aineistoissa. Tulokset ovat samanta-paisia, mutta hieman korkeampia moni-kuin yksikielisissa aineistoissa. Tutkitut kielet ovat komisyrjaani ja venaja. Tulosten yksityiskohtaisempi analyysi riippu-vuuksien mukaan osoittaa, etta tietyt kielikohtaiset suhteet on tunnistettu oikein jopa niiden esiintyessa luonnollisissa koodinvaihtoa sisaltavissa lauseissa. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details:

Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations