Publications

Semantics-enabled Query Performance Prediction for Ad hoc Table Retrieval

Maryam Khodabakhsh and Ebrahim Bagheri
Reference:
Maryam Khodabakhsh and Ebrahim Bagheri Semantics-enabled Query Performance Prediction for Ad hoc Table Retrieval. In Information Processing and Management, 2020.
Links to Publication: [pdf]
Abstract:
Predicting the performance of a retrieval method for a given query is a highly important and challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) plays an important role in real time handling of queries with varying levels of difficulty. While there have been several successful query performance predictors, no predictors have yet been introduced within the context of the ad hoc table retrieval task, which is concerned with answering a query with a ranked list of tables. In this paper, we propose to perform query performance prediction based on neural embedding techniques for ad hoc table retrieval and introduce three neural features. The neural features are based on neural embedding techniques and leverage the distance between tokens in the embedding space in order to capture their semantic similarity. We evaluate our proposed work based on a gold standard test collection and compare it with the state-of-the-art post-retrieval query performance prediction methods. We find that our neural features (1) are effective for predicting the performance of content-based ranking functions; and not as effective for feature-based ranking functions, and (2) show a synergistic impact on existing QPP methods and hence are able to increase their performance in practice.
Bibtex Entry:
@article{ipm2020f, title={Semantics-enabled Query Performance Prediction for Ad hoc Table Retrieval}, journal={Information Processing and Management}, author={Maryam Khodabakhsh and Ebrahim Bagheri}, abstract={Predicting the performance of a retrieval method for a given query is a highly important and challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) plays an important role in real time handling of queries with varying levels of difficulty. While there have been several successful query performance predictors, no predictors have yet been introduced within the context of the ad hoc table retrieval task, which is concerned with answering a query with a ranked list of tables. In this paper, we propose to perform query performance prediction based on neural embedding techniques for ad hoc table retrieval and introduce three neural features. The neural features are based on neural embedding techniques and leverage the distance between tokens in the embedding space in order to capture their semantic similarity. We evaluate our proposed work based on a gold standard test collection and compare it with the state-of-the-art post-retrieval query performance prediction methods. We find that our neural features (1) are effective for predicting the performance of content-based ranking functions; and not as effective for feature-based ranking functions, and (2) show a synergistic impact on existing QPP methods and hence are able to increase their performance in practice.}, year={2020}, webpdf={http://ls3.rnet.ryerson.ca/wiki/images/9/91/Table_QPP.pdf}, url{#} }




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