111 citations · 251 across the 18 of their papers we have counts for
7 papers · 1 filter
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Miao Fan, Yeqi Bai, Mingming Sun +1
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relat…
GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps
Yibo Sun, Jizhou Huang, Chunyuan Yuan +4
Providing timely accessibility reminders of a point-of-interest (POI) plays a vital role in improving user satisfaction of finding places and making visiting decisions. However, it…
Semi-Supervised Variational Reasoning for Medical Dialogue Generation
Dongdong Li, Zhaochun Ren, Pengjie Ren +4
Medical dialogue generation aims to provide automatic and accurate responses to assist physicians to obtain diagnosis and treatment suggestions in an efficient manner. In medical d…
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
Mingming Sun, Xu Li, Xin Wang +3
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus…
Distant Supervision for Entity Linking
Miao Fan, Qiang Zhou, Thomas Fang Zheng
Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding d…
Jointly Embedding Relations and Mentions for Knowledge Population
Miao Fan, Kai Cao, Yifan He +1
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 approache…
Errata: Distant Supervision for Relation Extraction with Matrix Completion
Miao Fan, Deli Zhao, Qiang Zhou +3
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and no…