Deep learning-based language models pretrained on large unannotated text corpora have
been demonstrated to allow efficient transfer learning for natural language processing, with …

Cross-linguistically consistent annotation is necessary for sound comparative evaluation
and cross-lingual learning experiments. It is also useful for multilingual system development …

Revisiting the now de facto standard Stanford dependency representation, we propose an
improved taxonomy to capture grammatical relations across languages, including …

Joint morphological and syntactic analysis has been proposed as a way of improving
parsing accuracy for richly inflected languages. Starting from a transition-based model for …

The world's languages exhibit striking diversity. At the same time, recurring linguistic
patterns suggest the possibility that this diversity is shaped by features of human cognition …

Large pretrained masked language models have become state-of-the-art solutions for many
NLP problems. The research has been mostly focused on English language, though. While …

We present a new manually annotated corpus for broad-coverage named entity recognition
for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 …

Purpose User-generated social media comments can be a useful source of information for
understanding online corporate reputation. However, the manual classification of these …

We investigate the problem of determining the predictive confidence (or, conversely,
uncertainty) of a neural classifier through the lens of low-resource languages. By training …

In this paper, we present a critical analysis of the dependency annotation framework used in
the METU-Sabancı Treebank (MST), and propose new annotation schemes that would …