TY - GEN
T1 - A signature approach to patent classification
AU - Seneviratne, Dilesha
AU - Geva, Shlomo
AU - Zuccon, Guido
AU - Ferraro, Gabriela
AU - Chappell, Timothy
AU - Meireles, Magali
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that intuitively document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.
AB - We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that intuitively document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.
UR - http://www.scopus.com/inward/record.url?scp=84958074476&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28940-3_35
DO - 10.1007/978-3-319-28940-3_35
M3 - Conference contribution
AN - SCOPUS:84958074476
SN - 9783319289397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 413
EP - 419
BT - Information Retrieval Technology - 11th Asia Information Retrieval Societies Conference, AIRS 2015, Proceedings
A2 - Scholer, Falk
A2 - Zuccon, Guido
A2 - Geva, Shlomo
A2 - Sun, Aixin
A2 - Joho, Hideo
A2 - Zhang, Peng
PB - Springer Verlag
T2 - 11th Asia Information Retrieval Societies Conference, AIRS 2015
Y2 - 2 December 2015 through 4 December 2015
ER -