TY - GEN
T1 - Unsupervised semantic similarity computation using web search engines
AU - Iosif, Elias
AU - Potamianos, Alexandras
PY - 2007
Y1 - 2007
N2 - In this paper, we propose two novel web-based metrics for semantic similarity computation between words. Both metrics use a web search engine in order to exploit the retrieved information for the words of interest. The first metric considers only the page counts returned by a search engine, based on the work of [1]. The second downloads a number of the top ranked documents and applies "wide-context" and "narrow-context" metrics. The proposed metrics work automatically, without consulting any human annotated knowledge resource. The metrics are compared with WordNet-based methods. The metrics' performance is evaluated in terms of correlation with respect to the pairs of the commonly used Charles - Miller dataset. The proposed "wide-context" metric achieves 71% correlation, which is the highest score achieved among the fully unsupervised metrics in the literature up to date.
AB - In this paper, we propose two novel web-based metrics for semantic similarity computation between words. Both metrics use a web search engine in order to exploit the retrieved information for the words of interest. The first metric considers only the page counts returned by a search engine, based on the work of [1]. The second downloads a number of the top ranked documents and applies "wide-context" and "narrow-context" metrics. The proposed metrics work automatically, without consulting any human annotated knowledge resource. The metrics are compared with WordNet-based methods. The metrics' performance is evaluated in terms of correlation with respect to the pairs of the commonly used Charles - Miller dataset. The proposed "wide-context" metric achieves 71% correlation, which is the highest score achieved among the fully unsupervised metrics in the literature up to date.
UR - http://www.scopus.com/inward/record.url?scp=48349085559&partnerID=8YFLogxK
U2 - 10.1109/WI.2007.104
DO - 10.1109/WI.2007.104
M3 - Conference contribution
AN - SCOPUS:48349085559
SN - 0769530265
SN - 9780769530260
T3 - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
SP - 381
EP - 387
BT - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
T2 - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Y2 - 2 November 2007 through 5 November 2007
ER -