Semantics-Based Document Categorization Employing Semi-Supervised Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43910951" target="_blank" >RIV/62156489:43110/17:43910951 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4018/978-1-5225-1759-7.ch077" target="_blank" >http://dx.doi.org/10.4018/978-1-5225-1759-7.ch077</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-5225-1759-7.ch077" target="_blank" >10.4018/978-1-5225-1759-7.ch077</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semantics-Based Document Categorization Employing Semi-Supervised Learning

Popis výsledku v původním jazyce

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as &quot;seeds&quot; to automatically label the remaining volume of unlabeled items.

Název v anglickém jazyce

Semantics-Based Document Categorization Employing Semi-Supervised Learning

Popis výsledku anglicky

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as &quot;seeds&quot; to automatically label the remaining volume of unlabeled items.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications

ISBN

978-1-5225-1759-7

Počet stran výsledku

29

Strana od-do

1884-1912

Počet stran knihy

3048

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

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