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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F14%3A43923364%21RIV15-GA0-23520___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007/978-3-319-11746-1_40
dcterms:description
The WISE 2014 challenge was concerned with the task of multi-label classification of articles coming from Greek print media. Raw data comes from the scanning of print media, article segmentation, and optical character segmentation, and therefore is quite noisy. Each article is examined by a human annotator and categorized to one or more of the topics being monitored. Topics range from specific persons, products, and companies that can be easily categorized based on keywords, to more general semantic concepts, such as environment or economy. Building multi-label classifiers for the automated annotation of articles into topics can support the work of human annotators by suggesting a list of all topics by order of relevance, or even automate the annotation process for media and/or categories that are easier to predict. This saves valuable time and allows a media monitoring company to expand the portfolio of media being monitored. This paper summarizes the approaches of the top 4 among the 121 teams that participated in the competition. The WISE 2014 challenge was concerned with the task of multi-label classification of articles coming from Greek print media. Raw data comes from the scanning of print media, article segmentation, and optical character segmentation, and therefore is quite noisy. Each article is examined by a human annotator and categorized to one or more of the topics being monitored. Topics range from specific persons, products, and companies that can be easily categorized based on keywords, to more general semantic concepts, such as environment or economy. Building multi-label classifiers for the automated annotation of articles into topics can support the work of human annotators by suggesting a list of all topics by order of relevance, or even automate the annotation process for media and/or categories that are easier to predict. This saves valuable time and allows a media monitoring company to expand the portfolio of media being monitored. This paper summarizes the approaches of the top 4 among the 121 teams that participated in the competition.
dcterms:title
WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics
skos:prefLabel
WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics
skos:notation
RIV/49777513:23520/14:43923364!RIV15-GA0-23520___
n4:aktivita
n7:P
n4:aktivity
P(GBP103/12/G084)
n4:dodaniDat
n16:2015
n4:domaciTvurceVysledku
n19:8780943
n4:druhVysledku
n20:D
n4:duvernostUdaju
n12:S
n4:entitaPredkladatele
n11:predkladatel
n4:idSjednocenehoVysledku
56259
n4:idVysledku
RIV/49777513:23520/14:43923364
n4:jazykVysledku
n5:eng
n4:klicovaSlova
multi-label classification, topic detection
n4:klicoveSlovo
n17:multi-label%20classification n17:topic%20detection
n4:kontrolniKodProRIV
[31E76B422E48]
n4:mistoKonaniAkce
Thessaloniki
n4:mistoVydani
Heidelberg
n4:nazevZdroje
Lecture Notes in Computer Science
n4:obor
n18:JD
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
9
n4:projekt
n15:GBP103%2F12%2FG084
n4:rokUplatneniVysledku
n16:2014
n4:tvurceVysledku
Qian, Weining Puurula, Antti Švec, Jan Semenov, Stanislav Vologiannidis, Stavros Tsoumakas, Grigorios Papadopoulos, Apostolos D'yakonov, Alexander Read, Jesse
n4:typAkce
n21:CST
n4:zahajeniAkce
2014-10-12+02:00
s:issn
0302-9743
s:numberOfPages
8
n13:doi
10.1007/978-3-319-11746-1_40
n10:hasPublisher
Springer-Verlag
n23:isbn
978-3-319-11745-4
n22:organizacniJednotka
23520