Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference
Abstract
This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.
Cite
Text
Jain et al. "Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/573Markdown
[Jain et al. "Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/jain2018ijcai-mitigating/) doi:10.24963/IJCAI.2018/573BibTeX
@inproceedings{jain2018ijcai-mitigating,
title = {{Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference}},
author = {Jain, Prachi and Murty, Shikhar and Mausam, and Chakrabarti, Soumen},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {4122-4129},
doi = {10.24963/IJCAI.2018/573},
url = {https://mlanthology.org/ijcai/2018/jain2018ijcai-mitigating/}
}