EXPERT_FINDING.md

Unsupervised, Efficient and Semantic Expertise Retrieval

Usage

To replicate the experiments of the paper on unsupervised and semantic expertise finding, have a look at this script which builds a log-linear model on the W3C collection. The script then evaluates the model on the 2005 and 2006 editions of TREC Enterprise track.

[cvangysel@ilps SERT] ./W3C-expert-finding.sh <path-to-W3C-corpus> <path-to-nonexisting-temporary-directory>

Verifying W3C corpus.

Creating output directory.

Fetching topics and relevance judgments.

Constructing log-linear model on W3C collection.

Evaluating on TREC Enterprise tracks.
2005 Enterprise Track: ndcg=0.5474; map=0.2603; recip_rank=0.6209; P_5=0.4098;
2006 Enterprise Track: ndcg=0.7883; map=0.4937; recip_rank=0.8834; P_5=0.7000;

Citation

If you use SERT to produce results for your scientific publication, please refer to our WWW 2016 paper on expert finding, our ICTIR 2017 paper on structural regularities in text-based vector spaces and our software overview paper:

@inproceedings{VanGysel2016experts,
  title={Unsupervised, Efficient and Semantic Expertise Retrieval},
  author={Van Gysel, Christophe and de Rijke, Maarten and Worring, Marcel},
  booktitle={WWW},
  volume={2016},
  pages={1069--1079},
  year={2016},
  organization={The International World Wide Web Conferences Steering Committee}
}

@inproceedings{VanGysel2017entityregularities,
  title={Structural Regularities in Text-based Entity Vector Spaces},
  author={Van Gysel, Christophe and de Rijke, Maarten and Kanoulas, Evangelos},
  booktitle={ICTIR},
  volume={2017},
  year={2017},
  organization={ACM}
}

@inproceedings{VanGysel2017sert,
  title={Semantic Entity Retrieval Toolkit},
  author={Van Gysel, Christophe and de Rijke, Maarten and Kanoulas, Evangelos},
  booktitle={SIGIR 2017 Workshop on Neural Information Retrieval (Neu-IR'17)},
  year={2017},
}