311 citations · 752 across the 16 of their papers we have counts for
5 papers · 1 filter
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura +4
We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issue…
So What's the Plan? Mining Strategic Planning Documents
Ekaterina Artemova, Tatiana Batura, Anna Golenkovskaya +5
In this paper we present a corpus of Russian strategic planning documents, RuREBus. This project is grounded both from language technology and e-government perspectives. Not only n…
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Anton Alekseev, Elena Tutubalina, Valentin Malykh +1
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.…
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov +3
The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named…
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
Juan M. Banda, Ramya Tekumalla, Guanyu Wang +6
As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled r…