Event detection is an important NLP task that has been only recently tackled in the context of Polish, mostly due to lack of language resources. The available annotated corpora are still relatively small and supervised learning approaches are limited by the size of training datasets. Event detection tools are very much needed, as they can be used to annotate more language resources automatically and to improve the accuracy of other NLP tasks, which rely on the detection of events, such as question answering or machine translation. In this paper we present a deep learning based approach to this task, which proved to capture the knowledge contained in the training data most effectively and outperform previously proposed methods. We show a direct comparison to previously published results, using the same data and experimental setup.

Part-of-Speech (POS) tagging is a crucial task in Natural Language Processing (NLP). POS tags may be assigned to tokens in text manually, by trained linguists, or using algorithmic approaches. Particularly, in the case of annotated text corpora, the quantity of textual data makes it unfeasible to rely on manual tagging and automated methods are used extensively. The quality of such methods is of critical importance, as even 1% tagger error rate results in introducing millions of errors in a corpus consisting of a billion tokens. In case of Polish several POS taggers have been proposed to date, but even the best of the taggers achieves an accuracy of ca. 93%, as measured on the one million subcorpus of the National Corpus of Polish (NCP). As the task of tagging is an example of classification, in this article we introduce a new POS tagger for Polish, which is based on the idea of combining several classifiers to produce higher quality tagging results than using any of the taggers individually.