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Short Text Categorization using World Knowledge

Türker, Rima

Abstract (englisch):

The content of the World Wide Web is drastically multiplying, and thus the amount of available online text data is increasing every day.
Today, many users contribute to this massive global network via online platforms by sharing information in the form of a short text. Such an immense amount of data covers subjects from all the existing domains (e.g., Sports, Economy, Biology, etc.). Further, manually processing such data is beyond human capabilities. As a result, Natural Language Processing (NLP) tasks, which aim to automatically analyze and process natural language documents have gained significant attention. Among these tasks, due to its application in various domains, text categorization has become one of the most fundamental and crucial tasks.

However, the standard text categorization models face major challenges while performing short text categorization, due to the unique characteristics of short texts, i.e., insufficient text length, sparsity, ambiguity, etc. In other words, the conventional approaches provide substandard performance, when they are directly applied to the short text categorization task. Furthermore, in the case of short text, the standard feature extraction techniques such as bag-of-words suffer from limited contextual information. ... mehr

Volltext §
DOI: 10.5445/IR/1000136814
Veröffentlicht am 27.08.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Publikationsdatum 27.08.2021
Sprache Englisch
Identifikator KITopen-ID: 1000136814
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xv, 140 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 03.03.2021
Referent/Betreuer Sack, H.
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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