Generating High-Quality Query Suggestion Candidates for Task-Based Search - ECIR'18
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README.md

Generating High-Quality Query Suggestion Candidates for Task-Based Search

This repository provides resources developed within the following paper:

H. Ding, S. Zhang, D. Garigliotti, and K. Balog. Generating High-Quality Query Suggestion Candidates for Task-Based Search, In ECIR'18, March 2018.

These resources allow to reproduce the results presented in the Task-based Query Suggestion paper.

This repository is structured as follows:

  • data/: TSV file used for evaluating the query suggestions. It was obtained by post-processing the test collection (details in the paper).

  • output/: all the final TSV run files, containing query suggestions generated by different methods and sources used in the paper.

Results

Results presented in the paper can be obtained by running the evaluation script, indicating the metrics of interest.

$ python eval.py 10  # P@10
$ python eval.py 20  # P@20

Crowdsourcing experiments

We seek to measure the quality of question suggestions for task-based search. Please see details below.

Experiment Layout

Citation

If you use the resources presented in this repository, please cite:

@InProceedings{Ding:2018:GHQ,
 author =     {Ding, Heng
   and Zhang, Shuo
   and Garigliotti, Dar{\'i}o
   and Balog, Krisztian},
 title =      {Generating High-Quality Query Suggestion Candidates for Task-Based Search},
 booktitle =  {Advances in Information Retrieval - Proceedings of the 40th European Conference on IR Research, ECIR 2018},
 year =       {2018},
 pages =      {625--631},
 publisher =  {Springer},
 abstract =   {We address the task of generating query suggestions for task-based search. The current state of the art relies heavily on suggestions provided by a major search engine. In this paper, we solve the task without reliance on search engines. Specifically, we focus on the first step of a two-stage pipeline approach, which is dedicated to the generation of query suggestion candidates. We present three methods for generating candidate suggestions and apply them on multiple information sources. Using a purpose-built test collection, we find that these methods are able to generate high-quality suggestion candidates.},
 doi =        {10.1007/978-3-319-76941-7_54},
}

Contact

Should you have any question, please contact Heng Ding at heng.ding[AT]whu.edu.cn (with [AT] replaced by @).