Automated Semantic Tagging of Textual Content

被引：21

作者：

Jovanovic, Jelena ^{[1
]}

Bagheri, Ebrahim ^{[2
]}

Cuzzola, John ^{[3
]}

Gasevic, Dragan ^{[4
]}

Jeremic, Zoran ^{[3
]}

Bashash, Reza ^{[5
]}

机构：

[1] Univ Belgrade, Belgrade 11001, Serbia

[2] Ryerson Univ, Lab Syst Software & Semant LS3, Toronto, ON, Canada

[3] Ryerson Univ, Toronto, ON, Canada

[4] Athabasca Univ, Athabasca, AB, Canada

[5] SideBuy Technol, Vancouver, BC, Canada

来源：

IT PROFESSIONAL | 2014年 / 16卷 / 06期

关键词：

intelligent systems; text analysis; natural language processing; artificial intelligence; computing methodologies; expert and knowledge-intensive system tools and techniques; software; data analysis;

D O I：

10.1109/MITP.2014.85

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. © 1999-2012 IEEE.

引用

页码：38 / 46

页数：9

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