@article{Athanaselisetal2011,
author = "Athanaselis, Theologos and Mamouras,  K. and Bakamidis, Stylianos and Dologlou, Ioannis",
abstract = "There are several reasons to expect that recognising word order errors in a text will be a difficult problem, and recognition rates reported in the literature are in fact low. Although grammatical rules constructed by computational linguists improve the performance of a grammar checker in word order diagnosis, the repairing task is still very difficult. This paper describes a method to repair any sentence with wrong word order using a statistical language model (LM). A good indicator of whether a person really knows a language is the ability to use the appropriate words in a sentence in correct word order. The “scrambled” words in a sentence produce a meaningless sentence. Most languages have a fairly fixed word order. This paper introduces a method, which is language independent, for repairing word order errors in sentences using the probabilities of most typical trigrams and bigrams extracted from a large text corpus such as the British National Corpus (BNC).",
journal = "International Journal on Artificial Intelligence Tools",
keywords = "Word order errors; statistical language model; permutations filtering; British National Corpus",
title = "{A} {C}orpus {B}ased {T}echnique {F}or {R}epairing {I}ll-Formed {S}entences {W}ith {W}ord {O}rder {E}rrors {U}sing  {C}o-Occurences {O}f {N}-Grams ",
year = "2011",
}