Introducing Baselines for Russian Named Entity Recognition

Gareev, Rinat; Tkachenko, Maksim; Solovyev, Valery D.; Simanovsky, Andrey; Ivanov, Vladimir

doi:10.1007/978-3-642-37247-6_27

Cited by 25 publications

(20 citation statements)

References 18 publications

(16 reference statements)

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“…To the best of our knowledge, several datasets for named entity recognition in the Russian language are available: the dataset, developed by Gareev et al [16], Persons 1000 and Collection 5 [30], [40], [42], FactRuEval 2016 [7], the Russian subset of the BSNLP Shared Task [33].…”

Section: Named Entity Recognition and Relation Extraction For The Rusmentioning

confidence: 99%

Rurebus-2020 Shared Task: Russian Relation Extraction for Business

Ivanin¹,

Abbyy²,

Artemova³

et al. 2020

Computational Linguistics and Intellectual Technologies

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In this paper, we present a shared task on core information extraction problems, named entity recognition and relation extraction. In contrast to popular shared tasks on related problems, we try to move away from strictly academic rigor and rather model a business case. As a source for textual data we choose the corpus of Russian strategic documents, which we annotated according to our own annotation scheme. To speed up the annotation process, we exploit various active learning techniques. In total we ended up with more than two hundred annotated documents. Thus we managed to create a high-quality data set in short time. The shared task consisted of three tracks, devoted to 1) named entity recognition, 2) relation extraction and 3) joint named entity recognition and relation extraction. We provided with the annotated texts as well as a set of unannotated texts, which could of been used in any way to improve solutions. In the paper we overview and compare solutions, submitted by the shared task participants. We release both raw and annotated corpora along with annotation guidelines, evaluation scripts and results at https://github.com/dialogue-evaluation/RuREBus.

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Section: Named Entity Recognition and Relation Extraction For The Rusmentioning

confidence: 99%

Rurebus-2020 Shared Task: Russian Relation Extraction for Business

Ivanin¹,

Abbyy²,

Artemova³

et al. 2020

Computational Linguistics and Intellectual Technologies

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“…(Pajzs et al, 2014) experimented with name lemmatisation and inflection variant generation in the highly inflected and agglutinative language Hungarian. (Gareev et al, 2013) describes NER for the highly inflective Russian language. The first edition of the Shared Task on Slavic NER was organised in the context of BSNLP 2017 (Piskorski et al, 2017) 5 Conclusions and Future Work…”

Section: Related Workmentioning

confidence: 99%

JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task

Jacquet¹,

Piskorski

Tanev³

et al. 2019

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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We report on the participation of the JRC Text Mining and Analysis Competence Centre (TMA-CC) in the BSNLP-2019 Shared Task, which focuses on named-entity recognition, lemmatisation and cross-lingual linking. We propose a hybrid system combining a rule-based approach and light ML techniques. We use multilingual lexical resources such as JRC-NAMES and BABELNET together with a named entity guesser to recognise names. In a second step, we combine known names with wild cards to increase recognition recall by also capturing inflection variants. In a third step, we increase precision by filtering these name candidates with automatically learnt inflection patterns derived from name occurrences in large news article collections. Our major requirement is to achieve high precision. We achieved an average of 65% Fmeasure with 93% precision on the four languages.

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Section: Related Workmentioning

confidence: 99%

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

2019

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The paper presents an unsupervised method for quickly extending a Ukrainian lexicon by generating paradigms and morphological feature structures for new proper names and neologisms, which are not covered by existing static morphological resources. This approach addresses a practical problem of modelling paradigms for entities created by the dynamic processes in the lexicon: this problem is especially serious for highly-inflected languages in domains with specialised or quickly changing lexicon. The method uses an unannotated Ukrainian corpus and a small fixed set of inflection tables, which can be found in traditional grammar textbooks. The advantage of the proposed approach is that updating the morphological lexicon does not require training or linguistic annotation, allowing fast knowledge-light extension of an existing static lexicon to improve morphological coverage on a specific corpus. The method is implemented in an open-source package on a GitHub repository. It can be applied to other low-resourced inflectional languages which have internet corpora and linguistic descriptions of their inflection system, following the example of inflection tables for Ukrainian. Evaluation results show consistent improvements in coverage for Ukrainian corpora of different corpus types.

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Introducing Baselines for Russian Named Entity Recognition

Cited by 25 publications

References 18 publications

Rurebus-2020 Shared Task: Russian Relation Extraction for Business

Rurebus-2020 Shared Task: Russian Relation Extraction for Business

JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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