Abstract
This study introduced a novel algorithm to compute similarities between natural languages. Using syntactical relationships derived from natural languages, the algorithm proposed a semantic pattern model and quantified natural languages using the word similarity technology based on WordNet and the Stanford Parser. Traditional IR technologies may not always determine the perfect matching without obvious relation or concept overlap between two natural language sentences. Some approaches deal with this problem via determining the order of words and the evaluation of semantic vectors; however, they were hard to be applied to compare the sentences with complex syntax as well as long sentences and sentences with arbitrary patterns and grammars. The proposed approach takes advantage of corpus-based ontology and grammatical rules to overcome this problem. The experimental results indicated that the algorithm could yield optimal results in semantic recognition when applied to sentences or short texts that are grammatically complex.
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