  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.
