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  • × author_ss:"Lu, Z."
  1. Liu, W.; Dog(an, R.I.; Kim, S.; Comeau, D.C.; Kim, W.; Yeganova, L.; Lu, Z.; Wilbur, W.J.: Author name disambiguation for PubMed (2014) 0.00 
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    Abstract
    Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false-positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state-of-the-art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state-of-the-art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine-learning method driven by a large-scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click-through-rate of PubMed users on author name query results improved from 34.9% to 36.9%.
    Type
    a
  2. Allan, J.; Ballesteros, L.; Callan, J.P.; Croft, W.B.; Lu, Z.: Recent experiment with INQUERY (1996) 0.00 
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    Type
    a
  3. Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z.: Comparing keywords plus of WOS and author keywords : a case study of patient adherence research (2016) 0.00 
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    Abstract
    Bibliometric analysis based on literature in the Web of Science (WOS) has become an increasingly popular method for visualizing the structure of scientific fields. Keywords Plus and Author Keywords are commonly selected as units of analysis, despite the limited research evidence demonstrating the effectiveness of Keywords Plus. This study was conceived to evaluate the efficacy of Keywords Plus as a parameter for capturing the content and scientific concepts presented in articles. Using scientific papers about patient adherence that were retrieved from WOS, a comparative assessment of Keywords Plus and Author Keywords was performed at the scientific field level and the document level, respectively. Our search yielded more Keywords Plus terms than Author Keywords, and the Keywords Plus terms were more broadly descriptive. Keywords Plus is as effective as Author Keywords in terms of bibliometric analysis investigating the knowledge structure of scientific fields, but it is less comprehensive in representing an article's content.
    Type
    a
  4. Lu, Z.; McKinley, K.S.: ¬The effect of collection organization and query locality on information retrieval system performance (2000) 0.00 
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    Abstract
    The explosion of content in distributed information retrieval (IR) systems requires new mechanisms in order to attain timely and accurate retrieval of text. Collection selection and partial collection replication with replica selection are two such mechanisms that enable IR systems to search a small percentage of data and thus improve performance and scalability. To maintain effectiveness as well as efficiency, IR systems must be configured carefully to consider workload locality and possible collection organizations. We propose IR system architectures that incorporate collection selection and partial replication, and compare configurations using a validated simulator. Locality and collection organization have dramatic effects on performance. For example, we demonstrate with simulation results that collection selection performs especially well when the distribution of queries to collections is uniform and collections are organized by topics, but it suffers when particular collections are "hot." We find that when queries have even modest locality, configurations that replicate data outperform those that partition data, usually significantly. These results can be used as the basis for IR system designs under a variety of workloads and collection organizations
    Type
    a

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