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Many popular machine learning techniques in natural language processing anddata mining rely heavily on high-quality text sources. However real-world textdatasets contain a significant amount of spelling errors and improperlypunctuated variants where the performance of these models would quicklydeteriorate. Moreover, real-world, web-scale datasets contain hundreds ofmillions or even billions of lines of text, where the existing text cleaningtools are prohibitively expensive to execute over and may require an overheadto learn the corrections. In this paper, we present FLAN, a scalable randomizedalgorithm to clean and canonicalize massive text data. Our algorithm relies onthe Jaccard similarity between words to suggest correction results. Weefficiently handle the pairwise word-to-word comparisons via Locality SensitiveHashing (LSH). We also propose a novel stabilization process to address theissue of hash collisions between dissimilar words, which is a consequence ofthe randomized nature of LSH and is exacerbated by the massive scale ofreal-world datasets. Compared with existing approaches, our method is moreefficient, both asymptotically and in empirical evaluations, and does not relyon additional features, such as lexical/phonetic similarity or word embeddingfeatures. In addition, FLAN does not require any annotated data or supervisedlearning. We further theoretically show the robustness of our algorithm withupper bounds on the false positive and false negative rates of corrections. Ourexperimental results on real-world datasets demonstrate the efficiency andefficacy of FLAN.

A Fast Randomized Algorithm for Massive Text Normalization