Accurate understanding of users' intentions is the key to domain specific question answering(QA)sys-tems while open domain QA systems can always make use of data redundancy technologies to improve performances. In this paper,we first propose a new robust constrained semantic grammar,which can resolve parsing ambiguities in word,syntax and semantic layers with the support of domain ontology.We then employ an efficient matching algo-rithm to deal with matchings inconsistent with the constraints of grammar rules.Finally,the candidate matchings are ranked based on several features,including density of matching words,historical matching accuracy of rules, matching relatedness and unrelatedness.In order to verify the validity of the proposed method,we apply the method to two domain-specific QA of different scales.The experimental results show that the proposed method is effective, the understanding accuracy rates are 82.4% and 86.2%,respectively,achieving the M RR values of 91.6% and 93.5%,respectively.%开放域问答系统通常可以借助一些数据冗余方法来提高问答准确性,而对于缺乏大规模领域语料的领域相关问答系统来说,准确理解用户的意图成为这类系统的关键.该文首先定义了一种带约束语义文法,与本体等语义资源相结合,可以在词汇级、句法级、语义级对自然语言句子的解析过程进行约束,解决自然语言理解歧义问题;然后给出了一个高效的文法匹配算法,其首先依据定义的各种约束条件预先过滤一些规则,然后依据提出的匹配度计算模型对候选的规则进行排序,找到最佳匹配.为了验证方法的有效性,将方法应用到两个实际的应用领域的信息查询系统.实验结果表明,本系统提出的方法切实有效,系统理解准确率分别达到了82.4% 和86.2%, M RR值分别达到了91.6% 和93.5%.
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