Automated Semantic Tagging of Textual Content

Abstract

Motivated by a continually increasing demand for applications that depend on machine comprehension of text-based content, researchers in both academia and industry have developed innovative solutions for automated information extraction from text. In this article, the authors focus on a subset of such tools--semantic taggers--that not only extract and disambiguate entities mentioned in the text but also identify topics that unambiguously describe the text's main themes. The authors offer insight into the process of semantic tagging, the capabilities and specificities of today's semantic taggers, and also indicate some of the criteria to be considered when choosing a tagger.