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Is there place for logic in recognizing textual entailment?

机译:识别文本蕴含之处是否存在逻辑?

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From a purely theoretical point of view, it makes sense to approach recognizing textual entailment (RTE) with the help of logic. After all, entailment matters are all about logic. In practice, only few RTE systems follow the bumpy road from words to logic. This is probably because it requires a combination of robust, deep semantic analysis and logical inference a?? and why develop something with this com- plexity if you perhaps can get away with something simpler? In this article, with the help of an RTE system based on Combinatory Cat- egorial Grammar, Discourse Representation Theory, and first-order theorem proving, we make an empirical assessment of the logic-based approach. High precision paired with low recall is a key characteristic of this system. The bottleneck in achieving high recall is the lack of a systematic way to produce relevant background knowledge. There is a place for logic in RTE, but it is (still) overshadowed by the knowledge acquisition problem.
机译:从纯理论的角度来看,借助逻辑方法来识别文本蕴含(RTE)是有意义的。归根结底,关键事项只与逻辑有关。实际上,只有很少的RTE系统遵循从单词到逻辑的坎road道路。这可能是因为它需要强大,深入的语义分析和逻辑推理的组合。如果您也许可以摆脱一些简单的事情,为什么还要开发具有这种复杂性的东西呢?在本文中,借助基于组合式语法,话语表示理论和一阶定理证明的RTE系统,我们对基于逻辑的方法进行了实证评估。高精度与低召回率是该系统的关键特性。实现高召回率的瓶颈在于缺乏产生相关背景知识的系统方法。 RTE中存在逻辑的地方,但是(仍然)被知识获取问题所掩盖。

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