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A PUT -Based Approach to Automatically Extracting Quantities and Generating Final Answers for Numerical Attributes

机译:一种基于PUT的方法,可自动提取数量并生成数字属性的最终答案

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Automatically extracting quantities and generating final answers for numerical attributes is very useful in many occasions, including question answering, image processing, human-computer interaction, etc. A common approach is to learn linguistics templates or wrappers and employ some algorithm or model to generate a final answer. However, building linguistics templates or wrappers is a tough task for builders. In addition, linguistics templates or wrappers are domain-dependent. To make the builder escape from building linguistics templates or wrappers, we propose a new approach to final answer generation based on Predicates-Units Table ( PUT ), a mini domain-independent knowledge base. It is deserved to point out that, in the following cases, quantities are not represented well. Quantities are absent of units. Quantities are perhaps wrong for a given question. Even if all of them are represented well, their units are perhaps inconsistent. These cases have a strong impact on final answer solving. One thousand nine hundred twenty-six real queries are employed to test the proposed method, and the experimental results show that the average correctness ratio of our approach is 87.1%.
机译:自动提取数量并生成数字属性的最终答案在许多情况下非常有用,包括问题回答,图像处理,人机交互等。一种常见的方法是学习语言学模板或包装器,并采用某种算法或模型来生成语言。最终答案。但是,构建语言学模板或包装对于构建者而言是一项艰巨的任务。另外,语言学模板或包装器是域相关的。为了使构建者摆脱构建语言模板或包装程序的困扰,我们提出了一种基于谓词-单位表(PUT)(一种与领域无关的微型知识库)的最终答案生成的新方法。值得指出的是,在以下情况下,数量不能很好地表示。数量缺少单位。对于一个给定的问题,数量也许是错误的。即使所有代表都很好,他们的单位也可能不一致。这些情况对最终答案的解决有很大影响。实验结果表明,该方法达到了196个实查询,平均正确率达87.1%。

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