The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

Piskorski, Jakub; Laskova, Laska; Marcińczuk, Michał; Pivovarova, Lidia; Přibáň, Pavel; Shinar, J.; Yangarber, Roman

doi:10.18653/v1/w19-3709

Cited by 30 publications

(33 citation statements)

References 32 publications

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“…However, we combine them in such a way that it can be seen as a generic multilingual NER system for a large number of languages (104 in total). Although top participants outperform our average score of 80.26% of "Relaxed Partial" (Piskorski et al, 2019), the strengths of the proposed strategy relays on the fact that it can be easily adapted to new languages and topics without extra effort.…”

Section: Resultsmentioning

confidence: 77%

See 1 more Smart Citation

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

Moreno¹,

Pontes²,

Coustaty³

et al. 2019

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

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This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60% to 83% in terms of Strict and Relaxed metrics, respectively.

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Section: Resultsmentioning

confidence: 77%

“…These topics include 1100 documents in the four languages. Further details can be found in the 2019 shared task overview paper (Piskorski et al, 2019). For training, we have used the documents provided for the task but also the ones in Czech, Polish, and Russian from the previous round of same task in 2017 (Piskorski et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

Moreno¹,

Pontes²,

Coustaty³

et al. 2019

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

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“…The BSNLP 2019 Shared Task [32] introduced a new multilingual dataset, annotated with named entities for four Slavic languages, Bulgarian, Czech, Polish, Russian. The named entities considered were persons, locations, organizations, events, products.…”

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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“…Two shared tasks were organized --the first and the second Multilingual Named Entity Challenge in Slavic Languages. They have been descibed in (Piskorski et al, 2017) and (Piskorski et al, 2019). The challenges included several tasks: recognition of mentions of named entities in Web documents in seven Slavic languages (Bulgarian, Croatian, Czech, Polish, Russian, Slovak, Slovene, Ukrainian), their normalization/lemmatization as well as cross-lingual linking.…”

Section: Related Workmentioning

confidence: 99%

A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

Simeonova¹,

Simov

Osenova

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

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We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-ofspeech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.

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The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

Cited by 30 publications

References 32 publications

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

Rurebus-2020 Shared Task: Russian Relation Extraction for Business

A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

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