activity
20172020
most citedReinforcement Learning for Relation Classification from Noisy Data

183 citations · 227 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2020

Learning to Check Contract Inconsistencies

Shuo Zhang, Junzhou Zhao, Pinghui Wang +5

Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carel…

cs.LG2020★ 3 cited

Unsupervised Adversarially-Robust Representation Learning on Graphs

Jiarong Xu, Yang Yang, Junru Chen +4

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generali…

cs.CL2018★ 7 cited

Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning

Jun Feng, Minlie Huang, Yijie Zhang +2

In this paper we address a task of relation mention extraction from noisy data: extracting representative phrases for a particular relation from noisy sentences that are collected…

cs.IR2018★ 183 cited

Reinforcement Learning for Relation Classification from Noisy Data

Jun Feng, Minlie Huang, Li Zhao +2

Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair…

cs.SI2018★ 2 cited

To Stay or to Leave: Churn Prediction for Urban Migrants in the Initial Period

Yang Yang, Zongtao Liu, Chenhao Tan +3

In China, 260 million people migrate to cities to realize their urban dreams. Despite that these migrants play an important role in the rapid urbanization process, many of them fai…

cs.IR2017★ 26 cited

Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation

Menghan Wang, Xiaolin Zheng, Yang Yang +1

This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar prefer…