Measuring Tree Similarity for Natural Language Processing Based Information Retrieval

Zhiwei Lin, Hui Wang, Sally McClean

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationNatural Language Processing and Information Systems
PublisherSpringer
Pages13-23
Volume6177/2
ISBN (Print)978-3-642-13880-5
DOIs
Publication statusPublished - 20 Jun 2010

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    Lin, Z., Wang, H., & McClean, S. (2010). Measuring Tree Similarity for Natural Language Processing Based Information Retrieval. In Natural Language Processing and Information Systems (Vol. 6177/2, pp. 13-23). Springer. https://doi.org/10.1007/978-3-642-13881-2_2