Automated Semantic Tagging of Textual Content

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.