A Fast Randomized Algorithm for Massive Text Normalization

TL;DR

FLAN is a scalable randomized algorithm that efficiently cleans and normalizes massive text datasets by leveraging Locality Sensitive Hashing and a novel stabilization process, improving over existing methods without needing annotated data.

Contribution

The paper introduces FLAN, a novel scalable randomized text normalization algorithm that uses LSH and a stabilization process, eliminating the need for supervised learning or additional features.

Findings

FLAN is more efficient than existing approaches.

FLAN effectively handles massive datasets with high accuracy.

Theoretical bounds demonstrate robustness of FLAN.

Abstract

Many popular machine learning techniques in natural language processing and data mining rely heavily on high-quality text sources. However real-world text datasets contain a significant amount of spelling errors and improperly punctuated variants where the performance of these models would quickly deteriorate. Moreover, real-world, web-scale datasets contain hundreds of millions or even billions of lines of text, where the existing text cleaning tools are prohibitively expensive to execute over and may require an overhead to learn the corrections. In this paper, we present FLAN, a scalable randomized algorithm to clean and canonicalize massive text data. Our algorithm relies on the Jaccard similarity between words to suggest correction results. We efficiently handle the pairwise word-to-word comparisons via Locality Sensitive Hashing (LSH). We also propose a novel stabilization process to address the issue of hash collisions between dissimilar words, which is a consequence of the randomized nature of LSH and is exacerbated by the massive scale of real-world datasets. Compared with existing approaches, our method is more efficient, both asymptotically and in empirical evaluations, and does not rely on additional features, such as lexical/phonetic similarity or word embedding features. In addition, FLAN does not require any annotated data or supervised learning. We further theoretically show the robustness of our algorithm with upper bounds on the false positive and false negative rates of corrections. Our experimental results on real-world datasets demonstrate the efficiency and efficacy of FLAN.