Publication - A Corpus Based Technique For Repairing Ill-Formed Sentences With Word Order Errors Using Co-Occurences Of N-Grams
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A Corpus Based Technique For Repairing Ill-Formed Sentences With Word Order Errors Using Co-Occurences Of N-Grams

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Journal article

 

Year: 2011
Authors: Theologos Athanaselis; K. Mamouras; Stylianos Bakamidis; Ioannis Dologlou
Journal: International Journal on Artificial Intelligence Tools
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).
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