OCR噪声对语言模型影响的评估
An Assessment of the Impact of OCR Noise on Language Models
摘要：
神经语言模型是现代自然语言处理应用的支柱。因此，它们在经过光学字符识别（OCR）的文本遗产收藏品中的使用也在增加。然而，我们对OCR噪声对语言模型的影响的理解仍然有限。我们使用荷兰语、英语、法语和德语的数据评估OCR噪声对各种语言模型的影响。我们发现，OCR噪声对语言建模构成了重大障碍，随着OCR质量的降低，语言模型越来越偏离其无噪声目标。在小语料库的情况下，包括PPMI和Word2Vec在内的简单模型在这方面始终优于基于transformer的模型。
英文摘要：
Neural language models are the backbone of modern-day natural language processing applications. Their use on textual heritage collections which have undergone Optical Character Recognition (OCR) is therefore also increasing. Nevertheless, our understanding of the impact OCR noise could have on language models is still limited. We perform an assessment of the impact OCR noise has on a variety of language models, using data in Dutch, English, French and German. We find that OCR noise poses a significant obstacle to language modelling, with language models increasingly diverging from their noiseless targets as OCR quality lowers. In the presence of small corpora, simpler models including PPMI and Word2Vec consistently outperform transformer-based models in this respect.
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。
