Reading The Web with Learned Syntactic-Semantic Inference Rules.
Empirical Methods in Natural Language Processing(2012)
摘要
We study how to extend a large knowledge base (Freebase) by reading relational information from a large Web text corpus. Previous studies on extracting relational knowledge from text show the potential of syntactic patterns for extraction, but they do not exploit background knowledge of other relations in the knowledge base. We describe a distributed, Web-scale implementation of a path-constrained random walk model that learns syntactic-semantic inference rules for binary relations from a graph representation of the parsed text and the knowledge base. Experiments show significant accuracy improvements in binary relation prediction over methods that consider only text, or only the existing knowledge base.
更多查看译文
关键词
inference,web,reading,rules,syntactic-semantic
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络