TY - GEN

T1 - Automatic classification of documents in cold-start scenarios

AU - Kawase, Ricardo

AU - Fisichella, Marco

AU - Nunes, Bernardo Pereira

AU - Ha, Kyung Hun

AU - Bick, Markus

PY - 2013

Y1 - 2013

N2 - Document classification is key to ensuring quality of any digital library. However, classifying documents is a very time-consuming task. In addition, few or none of the documents in a newly created repository are classified. The non-classification of documents not only prevents users from finding information but also hinders the system's aptitude to recommend relevant items. Moreover, the lack of classified documents prevents any kind of machine learning algorithm to automatically annotate these items. In this work, we propose a novel approach to automatically classifying documents that differs from previous works in the sense that it exploits the wisdom of the crowds available on theWeb. Our proposed strategy adapts an automatic tagging approach combined with a straightforward matching algorithm to classify documents in a given domain classification. To validate our findings, we compared our methods against the existing and performed a user evaluation with 61 participants to estimate the quality of the classifications. Results show that, in 72% of the cases, the automatic classification is relevant and well accepted by participants. In conclusion, automatic classification can facilitate access to relevant documents.

AB - Document classification is key to ensuring quality of any digital library. However, classifying documents is a very time-consuming task. In addition, few or none of the documents in a newly created repository are classified. The non-classification of documents not only prevents users from finding information but also hinders the system's aptitude to recommend relevant items. Moreover, the lack of classified documents prevents any kind of machine learning algorithm to automatically annotate these items. In this work, we propose a novel approach to automatically classifying documents that differs from previous works in the sense that it exploits the wisdom of the crowds available on theWeb. Our proposed strategy adapts an automatic tagging approach combined with a straightforward matching algorithm to classify documents in a given domain classification. To validate our findings, we compared our methods against the existing and performed a user evaluation with 61 participants to estimate the quality of the classifications. Results show that, in 72% of the cases, the automatic classification is relevant and well accepted by participants. In conclusion, automatic classification can facilitate access to relevant documents.

KW - Automatic classification

KW - Cold-start

KW - Digital libraries

KW - Information retrieval

KW - User evaluation

UR - http://www.scopus.com/inward/record.url?scp=84879751815&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84879751815

SN - 9781450318501

T3 - ACM International Conference Proceeding Series

BT - 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS 2013

T2 - 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS 2013

Y2 - 12 June 2013 through 14 June 2013

ER -