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  • × author_ss:"He, B."
  • × author_ss:"Ye, Z."
  1. Ye, Z.; Huang, J.X.; He, B.; Lin, H.: Mining a multilingual association dictionary from Wikipedia for cross-language information retrieval (2012) 0.00 
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    Abstract
    Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notable Web corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph-based approach to constructing a cross-language association dictionary (CLAD) from Wikipedia, which can be used in a variety of cross-language accessing and processing applications. In order to evaluate the quality of the mined CLAD, and to demonstrate how the mined CLAD can be used in practice, we explore two different applications of the mined CLAD to cross-language information retrieval (CLIR). First, we use the mined CLAD to conduct cross-language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standard CLIR test collections show that the CLIR retrieval performance can be substantially improved with the above two applications of CLAD, which indicates that the mined CLAD is of sound quality.
    Type
    a
  2. Ye, Z.; He, B.; Wang, L.; Luo, T.: Utilizing term proximity for blog post retrieval (2013) 0.00 
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    Abstract
    Term proximity is effective for many information retrieval (IR) research fields yet remains unexplored in blogosphere IR. The blogosphere is characterized by large amounts of noise, including incohesive, off-topic content and spam. Consequently, the classical bag-of-words unigram IR models are not reliable enough to provide robust and effective retrieval performance. In this article, we propose to boost the blog postretrieval performance by employing term proximity information. We investigate a variety of popular and state-of-the-art proximity-based statistical IR models, including a proximity-based counting model, the Markov random field (MRF) model, and the divergence from randomness (DFR) multinomial model. Extensive experimentation on the standard TREC Blog06 test dataset demonstrates that the introduction of term proximity information is indeed beneficial to retrieval from the blogosphere. Results also indicate the superiority of the unordered bi-gram model with the sequential-dependence phrases over other variants of the proximity-based models. Finally, inspired by the effectiveness of proximity models, we extend our study by exploring the proximity evidence between query terms and opinionated terms. The consequent opinionated proximity model shows promising performance in the experiments.
    Type
    a

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