Model description
This is a baseline model for named entity recognition trained on the cross-topic split of the SlavicNER corpus.
Resources and Technical Documentation
- Paper: Cross-lingual Named Entity Corpus for Slavic Languages, to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
Evaluation
Will appear soon
Usage
from transformers import pipeline
model = "SlavicNLP/slavicner-ner-cross-topic-large"
text = """Nie jest za późno, aby powstrzymać Brexit, a Wielka Brytania wciąż
może zmienić zdanie - powiedział przewodniczący Rady Europejskiej
eurodeputowanym w Strasburgu"""
pipe = pipeline("ner", model, aggregation_strategy="simple")
entities = pipe(text)
print(*entities, sep="\n")
# {'entity_group': 'EVT', 'score': 0.99720407, 'word': 'Brexit', 'start': 35, 'end': 41}
# {'entity_group': 'LOC', 'score': 0.9656372, 'word': 'Wielka Brytania', 'start': 45, 'end': 60}
# {'entity_group': 'ORG', 'score': 0.9977708, 'word': 'Rady Europejskiej', 'start': 115, 'end': 132}
# {'entity_group': 'LOC', 'score': 0.95184135, 'word': 'Strasburgu', 'start': 151, 'end': 161}
Citation
@inproceedings{piskorski-etal-2024-cross-lingual,
title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
author = "Piskorski, Jakub and
Marci{\'n}czuk, Micha{\l} and
Yangarber, Roman",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.369",
pages = "4143--4157",
abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
and mT5-large for named entity lemmatization and linking.",
}
Contact
Michał Marcińczuk (marcinczuk@gmail.com)