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Jointly Embedding Relations and Mentions for Knowledge Population
[article]
2015
arXiv
pre-print
This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of relation inference. It differs from most stand-alone approaches which separately operate on either knowledge bases or free texts. The proposed model simultaneously learns low-dimensional vector representations for both triplets in knowledge repositories and the mentions of relations in free texts, so that we can leverage the evidence both resources to make more accurate
arXiv:1504.01683v4
fatcat:jn5q52rj6fdxrkbmn6npt5jf7q