Despite being one of the most linguistically diverse groups of countries,
computational linguistics and language processing research in Southeast Asia
has struggled to match the level of countries from the Global North. Thus,
initiatives such as open-sourcing corpora and the development of baseline
models for basic language processing tasks are important stepping stones to
encourage the growth of research efforts in the field. To answer this call, we
introduce CebuaNER, a new baseline model for named entity recognition (NER) in
the Cebuano language. Cebuano is the second most-used native language in the
Philippines, with over 20 million speakers. To build the model, we collected
and annotated over 4,000 news articles, the largest of any work in the
language, retrieved from online local Cebuano platforms to train algorithms
such as Conditional Random Field and Bidirectional LSTM. Our findings show
promising results as a new baseline model, achieving over 70% performance on
precision, recall, and F1 across all entity tags, as well as potential efficacy
in a crosslingual setup with Tagalog.

为鼓励该领域的研究工作增长，本研究引入了 CebuaNER，该模型是用于 Cebuano 语言中命名实体识别（NER）的新基线模型。该模型在训练算法时收集并标注了超过 4,000 篇新闻文章，并在精确率、召回率和 F1 三个指标上取得了超过 70% 的良好表现，同时在与 Tagalog 语言的跨语言设置中显示出潜在的有效性。

CebuaNER：一个新的基准 Cebuano 命名实体识别模型

CebuaNER: A New Baseline Cebuano Named Entity Recognition Model

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。