Title
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
Abstract
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, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general domain corpora, and 2) the documents are written in a language other than English. Unlike expectations, the state-of-the-art transformer-based models show modest performance for both tasks, either when approached sequentially, or in an end-to-end fashion. Our experiments have demonstrated that fine-tuning on a large unlabeled corpora does not automatically yield significant improvement and thus we may conclude that more sophisticated strategies of leveraging unlabelled texts are demanded. In this paper, we describe the whole developed pipeline, starting from text annotation, baseline development, and designing a shared task in hopes of improving the baseline. Eventually, we realize that the current NER and RE technologies are far from being mature and do not overcome so far challenges like ours.
Year
DOI
Venue
2020
10.1007/978-3-030-72610-2_2
AIST
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Vitaly Ivanin100.34
Ekaterina Artemova203.72
Tatiana Batura301.35
Vladimir Ivanov418816.17
Veronika Sarkisyan500.34
Elena Tutubalina62012.61
Ivan Smurov700.68