A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek

Pranaydeep Singh, Gorik Rutten, Els Lefever
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引用次数: 16

Abstract

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT
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中古希腊语BERT语言建模与形态分析的初步研究
本文对古希腊语和拜占庭希腊语的自动语言预处理以及更具体的形态学分析进行了初步研究。为此,在现代、古代和后古典希腊文本的各种语料库的基础上,训练了一个新的基于子词的BERT语言模型。因此,将得到的BERT嵌入结合起来训练古希腊语和拜占庭希腊语的细粒度词性标注器。此外,还对一个希腊谚语语料库进行了手工注释,并使用所得到的金标准来评估形态分析器对拜占庭希腊语的性能。实验结果显示,BERT语言模型的困惑分数(4.9)非常好,域内数据(包含古典希腊语和中世纪希腊语混合的树库)的细粒度词性标注器以及新创建的拜占庭希腊语黄金标准数据集的最先进性能也非常好。语言模型和相关代码可在https://github.com/pranaydeeps/Ancient-Greek-BERT上使用
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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