用简单框架为濒危语言苏格兰盖尔语构建高效词性标注器。
A Modular Part-of-Speech Tagger for Scottish Gaelic using spaCy
- 基于spaCy框架，仅用标注语料训练模型，无需外部嵌入或预训练模型。
- 细粒度标签模型准确率达88.6%，粗粒度模型达93.7%。
- 适合低资源、形态复杂语言的轻量级自然语言处理研究者参考。
低资源语言的词性标注因标注数据有限而面临挑战，尤其是语言结构复杂的语种。苏格兰盖尔语（Gaidhlig）是一种形态丰富且濒危的语言，数字资源稀缺，适合作为轻量级语言处理方法的检验对象。本文利用spaCy自然语言处理框架，基于苏格兰盖尔语标注参考语料库（Annotated Reference Corpus of Scottish Gaelic），构建两个词性标注模型：一个采用细粒度标签集，另一个采用简化粗粒度标签集。两个模型均在极少预处理和配置下训练，不依赖外部词嵌入或预训练语言模型，仅通过监督学习完成。细粒度模型达到88.6%的标注准确率，粗粒度模型达93.7%。结果与此前发布的两个苏格兰盖尔语标注器相当，表明在低资源且形态复杂的语言环境下，简单现成的处理流程仍可取得良好效果。
原文摘要 · Abstract (English)
Part-of-speech tagging for low-resource languages remains challenging due to limited annotated data, especially for linguistically complex languages. Gaidhlig (Scottish Gaelic) is a morphologically rich and endangered language with limited digital resources, making it suitable for examining a lightweight language processing approach. This paper describes using the modular spaCy Natural Language Processing framework to build part-of-speech taggers for Gaidhlig using the Annotated Reference Corpus of Scottish Gaelic. We train two models with minimal pre-processing and configuration: one using a fine-grained tagset and another using a reduced coarse-grained tagset. Both models are trained without external embeddings or pre-trained language models, using only supervised learning from the available corpus. The fine-grained model achieves 88.6% tagging accuracy, while the coarse-grained model achieves 93.7%. The results are comparable to those of the two previously published Gaidhlig taggers, indicating that simple, off-the-shelf language processing pipelines can demonstrate good performance in low-resource and morphologically complex linguistic settings.
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