Reconstructing NER Corpora: a Case Study on Bulgarian
Iva Marinova, Laska Laskova, Petya Osenova, Kiril Simov, Alexander Popov
Abstract
The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.- Anthology ID:
- 2020.lrec-1.571
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4647–4652
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.571
- DOI:
- Bibkey:
- Cite (ACL):
- Iva Marinova, Laska Laskova, Petya Osenova, Kiril Simov, and Alexander Popov. 2020. Reconstructing NER Corpora: a Case Study on Bulgarian. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4647–4652, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Reconstructing NER Corpora: a Case Study on Bulgarian (Marinova et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.571.pdf
Export citation
@inproceedings{marinova-etal-2020-reconstructing,
title = "Reconstructing {NER} Corpora: a Case Study on {B}ulgarian",
author = "Marinova, Iva and
Laskova, Laska and
Osenova, Petya and
Simov, Kiril and
Popov, Alexander",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.571",
pages = "4647--4652",
abstract = "The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings %T Reconstructing NER Corpora: a Case Study on Bulgarian %A Marinova, Iva %A Laskova, Laska %A Osenova, Petya %A Simov, Kiril %A Popov, Alexander %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth Language Resources and Evaluation Conference %D 2020 %8 May %I European Language Resources Association %C Marseille, France %@ 979-10-95546-34-4 %G English %F marinova-etal-2020-reconstructing %X The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication. %U https://aclanthology.org/2020.lrec-1.571 %P 4647-4652
Markdown (Informal)
[Reconstructing NER Corpora: a Case Study on Bulgarian](https://aclanthology.org/2020.lrec-1.571) (Marinova et al., LREC 2020)
- Reconstructing NER Corpora: a Case Study on Bulgarian (Marinova et al., LREC 2020)
ACL
- Iva Marinova, Laska Laskova, Petya Osenova, Kiril Simov, and Alexander Popov. 2020. Reconstructing NER Corpora: a Case Study on Bulgarian. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4647–4652, Marseille, France. European Language Resources Association.