Lin, Zhiwei, Wang, Hui and McClean, Sally (2010) Measuring Tree Similarity for Natural Language Processing Based Information Retrieval. In: Natural Language Processing and Information Systems. Springer Berlin / Heidelberg, pp. 13-23. ISBN 978-3-642-13880-5 [Book section]
Full text not available from this repository.
Natural language processing based information retrieval (NIR) aims to go beyond the conventional bag-of-words based information retrieval (KIR) by considering syntactic and even semantic information in documents. NIR is a conceptually appealing approach to IR, but is hard due to the need to measure distance/similarity between structures. We aim to move beyond the state of the art in measuring structure similarity for NIR. In this paper, a novel tree similarity measurement dtwAcs is proposed in terms of a novel interpretation of trees as multi dimensional sequences. We calculate the distance between trees by the way of computing the distance between multi dimensional sequences, which is conducted by integrating the all common subsequences into the dynamic time warping method. Experimental result shows that dtwAcs outperforms the state of the art.
|Item Type:||Book section|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
|Deposited By:||Professor Sally McClean|
|Deposited On:||11 Aug 2010 15:42|
|Last Modified:||11 Aug 2010 15:42|
Repository Staff Only: item control page