# Developing a Named Entity Recognition Dataset for Tagalog

arXiv: https://arxiv.org/abs/2311.07161 · HTML: https://mlsanity.com/paper/2311.07161/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability](https://mlsanity.com/paper/2305.10266/) (2023)
- [Improving Large-scale Language Models and Resources for Filipino](https://mlsanity.com/paper/2111.06053/) (2021)
- [Dissecting Span Identification Tasks with Performance Prediction](https://mlsanity.com/paper/2010.02587/) (2020)
- [Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets](https://mlsanity.com/paper/2010.11574/) (2020)

## Built on by (most cited first, 7 of 7)

- [Batayan: A Filipino NLP benchmark for evaluating Large Language Models](https://mlsanity.com/paper/2502.14911/) (2025)
- [FilBench: Can LLMs Understand and Generate Filipino?](https://mlsanity.com/paper/2508.03523/) (2025)
- [Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages](https://mlsanity.com/paper/2509.02160/) (2025)
- [Extracting General-use Transformers for Low-resource Languages via Knowledge Distillation](https://mlsanity.com/paper/2501.12660/) (2025)
- [The UD-NewsCrawl Treebank: Reflections and Challenges from a Large-scale Tagalog Syntactic Annotation Project](https://mlsanity.com/paper/2505.20428/) (2025)
- [A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Models](https://mlsanity.com/paper/2604.04168/) (2026)
- [Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech](https://mlsanity.com/paper/2605.26007/) (2026)

## Abstract (arXiv)

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 kappa, 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.