实体属性槽填充是一种抽取命名实体特定属性(slot)实例(也称槽值,即filler)的自然语言处理研究。其中,“源信息”特指属性实例的来源,即一段或一句佐证实例正确反映属性的文本片断。观测语料可以发现,实体属性源信息中存在大量同质异构现象,即复述现象。因此,该文结合复述技术与现有知识库,探究了复述识别模型在仅有小规模种子“源信息”的基础上,对于实体属性槽源信息分类的有效性。实验证明,基于树编辑模型的复述识别方法在先验知识较少的情况下,能够很好地捕获实体属性的相关“源信息”。
Abstract
Slot Filling is to extract the value of specific slot (also called filler)for a named entity. Provenance refers to the source of the fillers, which it is usually a passage or a sentence used to prove that the filler correctly reflects the slot type. It is revealed in the corpus that there are many homogeneity in the provenance of slot filling, which is called paraphrase. Therefore, we combine the paraphrase technique with the existing knowledge base to explore the provenance identification via the paraphrase identification model derived from small-scale seed “provenance”. The results show that the paraphrase identification method based on tree edit model can capture the relevant “provenance” of slot fillers well with less prior knowledge.
关键词
槽填充 /
复述 /
树编辑模型
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Key words
slot filling /
paraphrasing /
tree edit model
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参考文献
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脚注
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基金
国家自然科学基金(61672367,61672368,61773276)
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