1 citations · 2 across the 4 of their papers we have counts for
4 papers
Automated Word Stress Detection in Russian
Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak +1
In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with L…
Char-RNN for Word Stress Detection in East Slavic Languages
Ekaterina Chernyak, Maria Ponomareva, Kirill Milintsevich
We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russia…
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
Gerhard Wohlgenannt, Ariadna Barinova, Dmitry Ilvovsky +1
Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but…
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings
Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky +2
In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models. The first ste…