A CORPUS BASED TECHNIQUE FOR REPAIRING ILL-FORMED SENTENCES WITH WORD ORDER ERRORS USING CO-OCCURRENCES OF N-GRAMS
Theologos Athanaselis, Konstantinos Mamouras, Stelios Bakamidis, Ioannis Dologlou · International Journal of Artificial Intelligence Tools · 2011
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).