Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns
Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns
复制标题
DOI：
--
复制
发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
Zornitsa Kozareva;E. Hovy
中科院分区：
文献类型：
--
作者：
Zornitsa Kozareva;E. Hovy
开放信息抽取和文本挖掘中的一个挑战性问题是语义关系选择性约束的学习。我们提出了一个最低限度的监督自举算法，使用一个单一的种子和递归的词汇语法模式学习的参数和超类型的一组不同的语义关系从Web。我们评估我们的算法的性能表示使用“动词”，“名词”，和“动词准备”的词汇句法模式的多个语义关系。基于人工评估的结果表明，采集信息的准确率约为90%。我们还将我们的结果与现有的知识库进行比较，以概述所收获的知识的粒度和多样性的相似性和差异。
A challenging problem in open information extraction and text mining is the learning of the selectional restrictions of semantic relations. We propose a minimally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the arguments and the supertypes of a diverse set of semantic relations from the Web. We evaluate the performance of our algorithm on multiple semantic relations expressed using "verb", "noun", and "verb prep" lexico-syntactic patterns. Human-based evaluation shows that the accuracy of the harvested information is about 90%. We also compare our results with existing knowledge base to outline the similarities and differences of the granularity and diversity of the harvested knowledge.