A Practical Approach for Relevance Measure of Inter-sentence

Many natural language processing tasks, such as text classification, text clustering, text summarization, and information retrieval etc., cannot miss the step-relevance measure of inter-sentence. However, many of the current NLP system always calculate not the inter-sentence relevance but their similarity. In fact, similarity means differently from relevance. The similarity measure can be acquired by comparing the exterior tokens of inter-sentences, but relevance measure can be obtained only by comparing the interior meaning of the sentences. In this paper, we described a method to explore the quantified conceptual relations of word-pairs by using the definition of a lexical item in modern Chinese standard dictionary, and proposed a practical approach to measure the inter-sentence relevance. The results of the examples show that our approach can solve the problem of how to measure the relevance of two sentences without (or very low) similarity but with a certain relevance. This method is also compatible with the current cosine similarity method.