ABSTRACT

A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels.

摘要

测量文本如何表示实体的一个关键挑战是需要将每个代表表达与一个相关实体相关联以产生有意义的结果。解决此问题的常用方法通常是基于接近方法，该方法需要大语料才能达到合理的准确性。我们展示了这种用于实体和表示形式之间关联的方法如何在表达级别产生高百分比的误报，而在文档级别产生低有效性。我们介绍了一种结合了语法分析，语义角色标记逻辑和机器学习方法（基于角色的关联方法）的解决方案。为了测试我们的方法，我们将其与流行的关联方法进行了比较，比较了两个相关实体（以色列国和巴勒斯坦权力机构）的新闻报道。