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  • × author_ss:"He, B."
  1. He, B.; Ounis, I.: Combining fields for query expansion and adaptive query expansion (2007) 0.00 
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    Abstract
    In this paper, we aim to improve query expansion for ad-hoc retrieval, by proposing a more fine-grained term reweighting process. This fine-grained process uses statistics from the representation of documents in various fields, such as their titles, the anchor text of their incoming links, and their body content. The contribution of this paper is twofold: First, we propose a novel query expansion mechanism on fields by combining field evidence available in a corpora. Second, we propose an adaptive query expansion mechanism that selects an appropriate collection resource, either the local collection, or a high-quality external resource, for query expansion on a per-query basis. The two proposed query expansion approaches are thoroughly evaluated using two standard Text Retrieval Conference (TREC) Web collections, namely the WT10G collection and the large-scale .GOV2 collection. From the experimental results, we observe a statistically significant improvement compared with the baselines. Moreover, we conclude that the adaptive query expansion mechanism is very effective when the external collection used is much larger than the local collection.
  2. 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.

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