Abstract
Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval quality. However, using general PRF mechanism would usually be demonstrated with poor performance when the retrieval objective is an event. Intuitively, event-oriented query often involves special properties of event object, which cannot easily be expressed with keyword-based event query, and might cause the deviation from target event to feedback documents. In this paper, an original, simple yet effective event-oriented PRF mechanism (EO-PRF) that takes into account the drawbacks of PRF mechanism from an event perspective to improve retrieval quality is proposed. This EO-PRF mechanism innovates by making use of some extra event knowledge to improve retrieval quality by integrating target event information with the initial query. Empirical evaluations based on TREC-TS 2015 dataset and standard benchmarks, namely mainstream non-feedback retrieval method, and state-of-the-art pseudo feedback methods, demonstrate the effectiveness of the proposed EO-PRF mechanism in event-oriented retrieval.
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Notes
- 1.
For the sake of efficiency, the language model presented in this paper is always a unigram language model.
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This is reasonable as, in general, we only need to confirm \(\theta _{e_{estimate}}\notin \{\theta _{e_i}\}_{i=1}^k\) if \(e_{estimate}=e_{u}\), however, the term distribution in whole document set can be seen as a mixture of multiple events, so it would not be consistent with any known event language model.
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Available at http://lucene.apache.org/.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (61572494), the National Key Research and Development Program of China (grant No. 2016YFB0801003), the National Natural Science Foundation of China (61462027) and the fund project of Jiangxi Province Education Office (GJJ160529).
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Wang, P., Li, P., Li, R., Wang, B. (2018). Improving Retrieval Quality Using PRF Mechanism from Event Perspective. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_49
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