Learning the costs for a string edit distance-based similarity measure for abbreviated language

Abstract

We present work in progress on word normalization for user-generated content. The approach is simple and helps in reducing the amount of manual annotation characteristic of more classical approaches. First, ortographic variants of a word, mostly abbreviations, are grouped together. From these manually grouped examples, we learn an automated classifier that, given a previously unseen word, determines whether it is an ortographic variant of a known word or an entirely new word. To do that, we calculate the similarity between the unseen word and all known words, and classify the new word as an ortographic variant of its most similar word. The classifier applies a string similarity measure based on the Levenshtein edit distance. To improve the accuracy of this measure, we assign edit operations an error-based cost. This scheme of cost assigning aims to maximize the distance between similar strings that are variants of different words. This custom similarity measure achieves an accuracy of .68, an important improvement if we compare it with the .54 obtained by the Levenshtein distance.