Intelligent typo correction for text mining through machine learning
Yinghao Huang, Yi Lu Murphey, Yao Ge · International Journal of Knowledge Engineering and Data Mining · 2015
Typo detection and correction is an important process in many text mining applications. This research focuses on automatic typo detection and correction for processing text documents that are unstructured, contain many grammar and spelling errors, and have many self-invented terminologies that can be interpreted only through domain-specific knowledge. In this paper we present an intelligent typo detection and correction (ITDC) system. Its 'intelligence' is reflected by automatically identifying and accurately correcting a broad range of typos, from simple typos such as duplication, omission, transposition, substitution characters, to complex spelling errors, such as word boundary errors, unconventional use of acronyms, etc. ITDC utilises general language knowledge and domain-specific knowledge extracted by machine learning algorithms. It is evaluated through a case study that involves the automatic processing of automotive fault diagnostic text documents. The experiment results show that the proposed system outperforms some of the state-of-art spell checking systems.