Aligning Semantic Graphs for Textual Inference and Machine Reading

Aligning Semantic Graphs for Textual Inference and Machine Reading
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对齐语义图以进行文本推理和机器阅读

DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Christopher D. Manning
Christopher D. Manning
中科院分区：
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文献类型：
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作者：
M. Marneffe;Trond Grenager;Bill MacCartney;Daniel Matthew Cer;Daniel Ramage;Chloé Kiddon;Christopher D. Manning

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本文介绍了我们在文本推理方面的工作，并将其置于机器阅读的更大目标的背景下。文本推理的任务是确定一个文本的含义是否可以从另一个文本的含义和背景知识中推断出来。我们的系统生成语义图作为文本意义的表示。本文给出了语义图对齐的新结果，并提出应用自然逻辑从对齐的语义图对中得出推理决策。我们认为这项工作是迈向一个能够展示广泛文本理解和学习的系统的第一步
This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning pairs of semantic graphs, and proposes the application of natural logic to derive inference decisions from those aligned pairs. We consider this work as first steps toward a system able to demonstrate broad-coverage text understanding and learning