Question Answering System (QA) allows users to ask question in natural language. Using natural language processing technology, QA returns answer to the user automatically. The paper used WordNet to construct lexical chains, and used the lexical chain in a question answering system based on the Internet. Using two kinds of different way calculates similarity of Snippets download from Google to sort them according to similarity. When getting for the first one and first ten Snippets, the accuracy of the answer snippets is increased by 13. 68% and 25. 4%. We made significant inspection for the experiment results. In significant level scheffe post-hoc 0. 05 conditions,p=0. 000078 is got and the accuracy of the system is dramatically improved.%自动问答系统以自然语言提出问题,并采用自然语言处理技术自动地将答案返回给用户.利用WordNet构建语义链,并将语义链用于问答系统.在面向Web的问答系统中,采用两种不同的计算文本相似度的方法对Google 返回的Snippets按照相似度进行排序,对返回的第一个和前十个Snippets中包含答案片段的情况进行分析,与不使用语义链时的情况相比,使包含答案片段的准确率分别提高了150％和66.12％.对实验结果进行了显著性检验,在显著性水准α=0.05的条件下,得到p=0.000078,使系统的准确率得到显著提高.
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