6 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.CL2020★ 6 cited
Global-to-Local Neural Networks for Document-Level Relation Extraction
Difeng Wang, Wei Hu, Ermei Cao +1
Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex…
cs.IR2020★ 1 cited
Open Knowledge Enrichment for Long-tail Entities
Ermei Cao, Difeng Wang, Jiacheng Huang +1
Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especiall…
cs.CL2018
Recurrent Skipping Networks for Entity Alignment
Lingbing Guo, Zequn Sun, Ermei Cao +1
We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA). Current methods use the embedding models mainly focusing on triple-level learning, wh…