Developing a Named Entity Recognition Dataset for Tagalog

Lester James V. Miranda
11/13/2023
cs.CL

No official code found. Best implementation: ljvmiranda921/calamanCy (71 stars, MIT, last commit 2025-07-20).

Abstract

We present the development of a Named Entity Recognition (NER) dataset for Tagalog. This corpus helps fill the resource gap present in Philippine languages today, where NER resources are scarce. The texts were obtained from a pretraining corpora containing news reports, and were labeled by native speakers in an iterative fashion. The resulting dataset contains ~7.8k documents across three entity types: Person, Organization, and Location. The inter-annotator agreement, as measured by Cohen's $κ$, is 0.81. We also conducted extensive empirical evaluation of state-of-the-art methods across supervised and transfer learning settings. Finally, we released the data and processing code publicly to inspire future work on Tagalog NLP.

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Code Implementations(1)

ljvmiranda921/calamanCyCommunity100%
MIT

NLP pipelines for Tagalog using spaCy

716HTML, ShellOct 17, 20221 years agoMIT
computational-linguisticslow-resource-languageslow-resource-nlpmachine-learningnatural-language-processing+3 more

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Cite this paper

@article{miranda2023developing,
  title  = {Developing a Named Entity Recognition Dataset for Tagalog},
  author = {Lester James V. Miranda},
  year   = {2023},
  eprint = {2311.07161},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.07161v1}
}

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