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Fuzzy TF-IDF Weighting in Synonym for Diabetes Question and Answers

机译:模糊TF-IDF在糖尿病问答的同义词中加权

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Currently, the synonyms are a problem to retrieved answer from question answering systems. A fuzzy based similarity is one method that many researchers used to solve this problem. This paper applied the fuzzy method with TF-IDF weighting in considering the alphabet of words in order to analysis a similarity between words. Our corpus consists of five hundred answers collected from reliable medical resources. Several fuzzy conditions were investigated to find out the best condition for answering the question. To evaluate our proposed method, thirty frequently asked questions are tested and compared to experts answers. The results showed that the acceptable answers were discovered on 80% words similarity above (fuzzy degree is greater than 0.8) with 80.09% or more of precision.
机译:目前,该同义词是从问题应答系统中检索答案的问题。基于模糊的相似性是许多研究人员用来解决这个问题的方法。本文在考虑单词字母表中应用TF-IDF加权的模糊方法,以分析单词之间的相似性。我们的语料库包括从可靠的医疗资源收集的五百答案。调查了几种模糊条件,以了解回答问题的最佳条件。为了评估我们所提出的方法,测试了三十个常见问题,并与专家答案进行比较。结果表明,可接受的答案在80%的单词相似之处(模糊程度大于0.8),精度为80.09%或更多。

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