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.

我们为塔加洛语开发了一个命名实体识别（NER）数据集，填补了菲律宾语言中 NER 资源匮乏的空白。文本来源于包含新闻报道的预训练语料库，并由母语人士迭代标注。该数据集包含约 7.8k 个文档，涵盖人名、组织和地点三个实体类型。我们还在有监督学习和迁移学习环境中对最先进的方法进行了广泛的实证评估。最后，我们公开发布了数据和处理代码，以激励未来在塔加洛语自然语言处理方面的研究工作。

为塔加洛语开发命名实体识别数据集

Developing a Named Entity Recognition Dataset for Tagalog

In this study, we developed the first baseline readability model for the
Cebuano language. Cebuano is the second most-used native language in the
Philippines with about 27.5 million speakers. As the baseline, we extracted
traditional or surface-based features, syllable patterns based from Cebuano's
documented orthography, and neural embeddings from the multilingual BERT model.
Results show that the use of the first two handcrafted linguistic features
obtained the best performance trained on an optimized Random Forest model with
approximately 87% across all metrics. The feature sets and algorithm used also
is similar to previous results in readability assessment for the Filipino
language showing potential of crosslingual application. To encourage more work
for readability assessment in Philippine languages such as Cebuano, we
open-sourced both code and data.

本论文针对菲律宾第二大母语 —— 宿务语，开发了首个基线可读性模型；本文提取了传统或基于表面的特征、基于 Cebuano 拼字书写的音节模式以及多语言 BERT 模型的神经嵌入。结果显示，使用前两个手工语言特征的随机森林模型训练表现最佳，并且使用的特征集和算法类似于以前为菲律宾语可读性评估所获得的结果，表明具有跨语言应用的潜力；为了鼓励在菲律宾诸如宿务语等语言的可读性评估方面进行更多工作，我们公开了代码和数据。