TY - GEN
T1 - WISE 2014 challenge
T2 - Multi-label classification of print media articles to topics
AU - Tsoumakas, Grigorios
AU - Papadopoulos, Apostolos
AU - Qian, Weining
AU - Vologiannidis, Stavros
AU - D’yakonov, Alexander
AU - Puurula, Antti
AU - Read, Jesse
AU - Švec, Jan
AU - Semenov, Stanislav
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84921684939
U2 - 10.1007/978-3-319-11746-1_40
DO - 10.1007/978-3-319-11746-1_40
M3 - 会议稿件
AN - SCOPUS:84921684939
SN - 9783319117454
T3 - Lecture Notes in Computer Science
SP - 2 of 2
BT - Web Information Systems Engineering – WISE 2014 - 15th International Conference, Proceedings
PB - Springer Verlag
ER -