Dataset Information

DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect.

ABSTRACT: DarNERcorp is a manually annotated named entity recognition (NER) dataset in the Moroccan dialect, also called Darija. The dataset consists of 65,905 tokens and their corresponding tags according to BIO scheme. 13.8% of the tokens are named entities spanning four categories: person, location, organization, and miscellaneous. The data were scraped from the Moroccan Dialect section of Wikipedia and processed and annotated using open-source libraries and tools. The data are useful for the Arabic natural language processing (NLP) community as they address the lack in dialectal Arabic annotated corpora. This dataset can be used to train and evaluate named entity recognition systems in dialectal and mixed Arabic.

SUBMITTER: Moussa HN

PROVIDER: S-EPMC10293988 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect.

Moussa Hanane Nour HN Mourhir Asmaa A

Data in brief 20230512

DarNERcorp is a manually annotated named entity recognition (NER) dataset in the Moroccan dialect, also called Darija. The dataset consists of 65,905 tokens and their corresponding tags according to BIO scheme. 13.8% of the tokens are named entities spanning four categories: person, location, organization, and miscellaneous. The data were scraped from the Moroccan Dialect section of Wikipedia and processed and annotated using open-source libraries and tools. The data are useful for the Arabic na ...[more]

PMID: 37383818

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Project description:MotivationThe explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER.ResultsWe demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target datasets with a small number of labels (approximately 6000 or less).Availability and implementationSource code for the LSTM-CRF is available at https://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available at https://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect.

Publications

DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect.

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