A Proposal of Automatic Error Correction in Text

TL;DR

This paper presents an automatic error correction system for electronic texts, combining error detection, candidate correction generation, and selection, using linguistic and statistical methods for Spanish to improve text accuracy for various applications.

Contribution

It introduces a novel approach integrating part-of-speech tagging, word similarity, dictionaries, and n-gram language models for Spanish text correction.

Findings

Effective error detection and correction in Spanish texts

Improved accuracy over baseline methods

Potential applications in OCR and text processing

Abstract

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish.