TY - GEN
T1 - Automatically weighting tags in XML collection
AU - Liu, Dexi
AU - Wan, Changxuan
AU - Chen, Lei
AU - Liu, Xiping
PY - 2010
Y1 - 2010
N2 - In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly.
AB - In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly.
KW - Tag weighting model
KW - Topic generalization
KW - XML retrieval
UR - https://openalex.org/W2064714439
UR - https://www.scopus.com/pages/publications/78651314897
U2 - 10.1145/1871437.1871603
DO - 10.1145/1871437.1871603
M3 - Conference Paper published in a book
SN - 9781450300995
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1289
EP - 1292
BT - CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
T2 - 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Y2 - 26 October 2010 through 30 October 2010
ER -