tchen7/entityduetneuralranking

Entity-Duet Neural Ranking Model

Entity-Duet Neural Ranking Model

There are source codes for Entity-Duet Neural Ranking Model (EDRM) Paper.

Baselines

There are codes for our main baselines: K-NRM and Conv-KNRM.

• End-to-end neural ad-hoc ranking with kernel pooling
• Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

EDRM

There are codes for our work based on Conv-KNRM.

Results

The ranking results. All results are in trec format.

Method	Testing-SAME (NDCG@1)	Testing-SAME (NDCG@10)	Testing-DIFF (NDCG@1)	Testing-DIFF (NDCG@10)	Testing-RAW (MRR)
K-NRM	0.2645	0.4197	0.3000	0.4228	0.3447
Conv-KNRM	0.3357	0.4810	0.3384	0.4318	0.3582
EDRM-KNRM	0.3096	0.4547	0.3327	0.4341	0.3616
EDRM-CKNRM	0.3397	0.4821	0.3708	0.4513	0.3892

Results on ClueWeb09 and CluWeb12. All models are trained on Anchor-Doc pairs in ClueWeb. These results only leverage entity embedding and entity description. For EDRM of English version, please refer to our OpenMatch tookit.

ClueWeb09:

Method	NDCG@20	ERR@20
Conv-KNRM	0.2893	0.1521
EDRM	0.2922	0.1642

ClueWeb12:

Method	NDCG@20	ERR@20
Conv-KNRM	0.1142	0.0930
EDRM	0.1183	0.0968

Citation

@inproceedings{liu2018EntityDuetNR,
  title={Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval},
  author={Zhenghao Liu and Chenyan Xiong and Maosong Sun and Zhiyuan Liu},
  booktitle={Proceedings of ACL},
  year={2018}
}

Contact

If you have questions, suggestions and bug reports, please email

[email protected].