@inproceedings{26011bd9433c43528e5a870d3374abb2,
title = "Deep neural learning approaches for Latvian morphological tagging",
abstract = "This paper describes ongoing research on improvements of morphological analysis, disambiguation and POS tagging for the Latvian language. Authors apply recent advances in sequential tagging with neural networks and word embeddings calculated from unlabeled corpus to improve morphological tagging accuracy. These approaches allow to reduce the fine-grained morphological tag word error rate from 7.9\% of earlier best systems to 6.2\%, and coarse-grained POS tag error rate from 3.6\% to 2.2\%.",
keywords = "Deep learning, Morphology, Neural networks, Tagging",
author = "Peteris Paikens",
note = "Publisher Copyright: {\textcopyright} 2016 The authors and IOS Press.; 7th International Conference on Human Language Technologies - The Baltic Perspective, Baltic HLT 2016 ; Conference date: 06-10-2016 Through 07-10-2016",
year = "2016",
doi = "10.3233/978-1-61499-701-6-160",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "160--166",
editor = "Inguna Skadina and Roberts Rozis",
booktitle = "Human Language Technologies - The Baltic Perspective",
address = "Netherlands",
}