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  • × author_ss:"Lu, W."
  1. Lu, W.; Ding, H.; Jiang, J.: ¬A document expansion framework for tag-based image retrieval (2018) 0.04 
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    Abstract
    Purpose The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR). Design/methodology/approach The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate). Findings The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD. Research limitations/implications Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach. Originality/value Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.
    Date
    20. 1.2015 18:30:22
  2. Lu, W.; MacFarlane, A.; Venuti, F.: Okapi-based XML indexing (2009) 0.02 
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    Abstract
    Purpose - Being an important data exchange and information storage standard, XML has generated a great deal of interest and particular attention has been paid to the issue of XML indexing. Clear use cases for structured search in XML have been established. However, most of the research in the area is either based on relational database systems or specialized semi-structured data management systems. This paper aims to propose a method for XML indexing based on the information retrieval (IR) system Okapi. Design/methodology/approach - First, the paper reviews the structure of inverted files and gives an overview of the issues of why this indexing mechanism cannot properly support XML retrieval, using the underlying data structures of Okapi as an example. Then the paper explores a revised method implemented on Okapi using path indexing structures. The paper evaluates these index structures through the metrics of indexing run time, path search run time and space costs using the INEX and Reuters RVC1 collections. Findings - Initial results on the INEX collections show that there is a substantial overhead in space costs for the method, but this increase does not affect run time adversely. Indexing results on differing sized Reuters RVC1 sub-collections show that the increase in space costs with increasing the size of a collection is significant, but in terms of run time the increase is linear. Path search results show sub-millisecond run times, demonstrating minimal overhead for XML search. Practical implications - Overall, the results show the method implemented to support XML search in a traditional IR system such as Okapi is viable. Originality/value - The paper provides useful information on a method for XML indexing based on the IR system Okapi.
  3. Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023) 0.00 
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    Date
    22. 1.2023 18:40:36
  4. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
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    Date
    22. 6.2023 14:55:20

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