Abstract
The previous work has justified the assumption that document ranking can be improved by further considering the coarse-grained relations in various linguistic levels (e.g., lexical, syntactical and semantic). To the best of our knowledge, little work is reported to incorporate the fine-grained ontological relations (e.g., <cannabis, TREATS, cancer>) in document ranking. Two contributions are worth noting in this work. First, three major combination models (i.e., summation, multiplication, and amplification) are designed to re-calculate the query-document relevance score considering both the term-level Okapi BM25 relevance score and the relation-level relevance score. Second, a vector-based scoring algorithm is proposed to calculate the relation-level relevance score. A few experiments on medical document ranking with CLEF2013 eHealth Lab medical information retrieval dataset show that the proposed document ranking algorithms can be further improved by incorporating the fine-grained ontological relations.
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Xia, Y., Xie, Z., Zhang, Q., Wang, H., Zhao, H. (2014). Cannabis_TREATS_cancer: Incorporating Fine-Grained Ontological Relations in Medical Document Ranking. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_25
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DOI: https://doi.org/10.1007/978-3-662-45924-9_25
Publisher Name: Springer, Berlin, Heidelberg
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