论文标题
Lokahi原型：从文本中自动提取实体关系模型
The Lokahi Prototype: Toward the automatic Extraction of Entity Relationship Models from Text
论文作者
论文摘要
实体关系提取设想，通过自动识别实体，实体协会以形成关系以及通过对这些实例进行分类以将它们分配给实体集（或类）和关系集（或关联），从文本集合，自动识别实体，实体协会进行自动生成语义数据模型。作为朝这个方向发展的第一步，Lokahi原型可以根据TF*IDF度量提取实体，并基于文档级的共发生统计量产生语义关系，例如，可能性比率和点式相互信息。本文介绍了探索性，典型，定性和合成研究的结果，总结了两个研究项目的见解，并基于这一点，表明了在实体关系领域从文本中提取的进一步研究的概述。
Entity relationship extraction envisions the automatic generation of semantic data models from collections of text, by automatic recognition of entities, by association of entities to form relationships, and by classifying these instances to assign them to entity sets (or classes) and relationship sets (or associations). As a first step in this direction, the Lokahi prototype can extract entities based on the TF*IDF measure, and generate semantic relationships based on document-level co-occurrence statistics, for example with likelihood ratios and pointwise mutual information. This paper presents results of an explorative, prototypical, qualitative and synthetic research, summarizes insights from two research projects and, based on this, indicates an outline for further research in the field of entity relationship extraction from text.
