Publications

An Extensible Toolkit of Query Refinement Methods and Gold Standard Dataset Generation

Hossein Fani and Mahtab Tamannaee and Fattane Zarrinkalam and Jamil Samouh and Samad Paydar and Ebrahim Bagheri
Reference:
Hossein Fani; Mahtab Tamannaee; Fattane Zarrinkalam; Jamil Samouh; Samad Paydar and Ebrahim Bagheri An Extensible Toolkit of Query Refinement Methods and Gold Standard Dataset Generation. In 43rd European Conference on IR Research (ECIR 2021), 2021.
Links to Publication:
Abstract:
We present an open-source extensible python-based toolkit that provides access to a (1) range of built-in unsupervised query expansion methods, and (2) pipeline for generating gold standard datasets for building and evaluating supervised query refinement methods. While the information literature offers abundant work on query expansion techniques, there is yet to be a tool that provides unified access to a comprehensive set of query expansion techniques. The advantage of our proposed toolkit, known as ReQue (refining queries), is that it offers one-stop shop access to query expansion techniques to be used in external information retrieval applications. More importantly, we show how ReQue can be used for building gold standards datasets that can be used for training supervised deep learning-based query refinement techniques. These techniques require sizeable gold query refinement datasets, which are not available in the literature. Reque provides the means to systematically build such datasets.
Bibtex Entry:
@inproceedings{ecir2021a, author = {Hossein Fani and Mahtab Tamannaee and Fattane Zarrinkalam and Jamil Samouh and Samad Paydar and Ebrahim Bagheri}, title = {An Extensible Toolkit of Query Refinement Methods and Gold Standard Dataset Generation}, booktitle = {43rd European Conference on IR Research (ECIR 2021)}, year = {2021}, abstract = {We present an open-source extensible python-based toolkit that provides access to a (1) range of built-in unsupervised query expansion methods, and (2) pipeline for generating gold standard datasets for building and evaluating supervised query refinement methods. While the information literature offers abundant work on query expansion techniques, there is yet to be a tool that provides unified access to a comprehensive set of query expansion techniques. The advantage of our proposed toolkit, known as ReQue (refining queries), is that it offers one-stop shop access to query expansion techniques to be used in external information retrieval applications. More importantly, we show how ReQue can be used for building gold standards datasets that can be used for training supervised deep learning-based query refinement techniques. These techniques require sizeable gold query refinement datasets, which are not available in the literature. Reque provides the means to systematically build such datasets.} }




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