古典法语戏剧词形还原与词性标注的语料库与模型
Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre
AI总结：
本文构建了古典法语戏剧的标注语料库，并训练了基于神经网络和CRF的模型，在领域内测试中达到超越现有技术的准确率，且对20世纪小说等外部数据具有鲁棒性。
摘要：
本文描述了构建带注释语料库及训练模型的过程，这些模型用于古典法语文学，重点关注戏剧，尤其是诗体喜剧。该工作最初是作为Cafiero和Camps [2019]中提出的文体计量分析的初步步骤而开发的。使用基于神经网络的最新词形还原器和CRF标注器，在领域内测试中实现了超越当前最先进水平的准确率，并在领域外测试（即直至20世纪的小说）中表现出鲁棒性。
英文摘要：
This paper describes the process of building an annotated corpus and training models for classical French literature, with a focus on theatre, and particularly comedies in verse. It was originally developed as a preliminary step to the stylometric analyses presented in Cafiero and Camps [2019]. The use of a recent lemmatiser based on neural networks and a CRF tagger allows to achieve accuracies beyond the current state-of-the art on the in-domain test, and proves to be robust during out-of-domain tests, i.e.up to 20th c.novels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
