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Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models.

, , , , and . CoRR, (2019)

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Neural Network and Random Forest Models in Protein Function Prediction., , , , , , , and . IEEE ACM Trans. Comput. Biol. Bioinform., 19 (3): 1772-1781 (2022)Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation., , , , and . BMC Bioinform., (2005)Building a Large Automatically Parsed Corpus of Finnish., , , , , , and . NODALIDA, volume 85 of Linköping Electronic Conference Proceedings, page 291-300. Linköping University Electronic Press, (2013)Identifying gender bias in blockbuster movies through the lens of machine learning., , , , , and . CoRR, (2022)Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks., , and . Nat. Lang. Eng., 27 (5): 545-574 (2021)UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations., , and . SemEval@COLING, page 807-811. The Association for Computer Linguistics, (2014)All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning, , , , , and . BMC bioinformatics, 9 (Suppl 11): S2 (2008)Detecting Sequential Genre Change in Eighteenth-Century Texts., , , , , and . CHR, volume 3290 of CEUR Workshop Proceedings, page 243-255. CEUR-WS.org, (2022)Enhancing Universal Dependency Treebanks: A Case Study., , , , , , and . UDW@EMNLP, page 102-107. Association for Computational Linguistics, (2018)Deep Learning, Film History: Model Explanation Techniques in the Analysis of Temporality in Finnish Fiction Film Metadata., , , , and . DHNB, volume 3232 of CEUR Workshop Proceedings, page 50-62. CEUR-WS.org, (2022)