Impact of imperfect OCR on part-of-speech tagging

Abstract

Part-of-speech (POS) tagging is the foundation of natural language processing (NLP) systems, and thus has been an active area of research for many years. However, one question remains unanswered: How will a POS tagger behave when the input text is not error-free? This issue can be of great importance when the text comes from imperfect sources like optical character recognition (OCR). This paper analyzes the performance of both individual POS taggers and combination systems on imperfect text. Experimental results show that a POS tagger's accuracy decreases linearly with the character error rate and the slope indicates a tagger's sensitivity to input text errors.