Past research has identified a rich set of handcrafted linguistic features
that can potentially assist various tasks. However, their extensive number
makes it difficult to effectively select and utilize existing handcrafted
features. Coupled with the problem of inconsistent implementation across
research works, there has been no categorization scheme or generally-accepted
feature names. This creates unwanted confusion. Also, most existing handcrafted
feature extraction libraries are not open-source or not actively maintained. As
a result, a researcher often has to build such an extraction system from the
ground up.
We collect and categorize more than 220 popular handcrafted features grounded
on past literature. Then, we conduct a correlation analysis study on several
task-specific datasets and report the potential use cases of each feature.
Lastly, we devise a multilingual handcrafted linguistic feature extraction
system in a systematically expandable manner. We open-source our system for
public access to a rich set of pre-implemented handcrafted features. Our system
is coined LFTK and is the largest of its kind. Find it at
github.com/brucewlee/lftk.

本文采用分类方法整理 220 余种手工语言特征，对几个特定的数据集进行相关性分析，整合设计了一个可扩展的跨语言手工语言特征提取系统 LFTK，并将其作为开源项目，以便科研人员和开发人员能够轻松使用。

LFTK：计算语言学中的手工特征

LFTK: Handcrafted Features in Computational Linguistics

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。