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
liuzhenghao0819@gmail.com.