activity
20182022
most citedScalable Rule-Based Representation Learning for Interpretable Classification

22 citations · 40 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CL20222 cited

Joint Open Knowledge Base Canonicalization and Linking

Yinan Liu, Wei Shen, Yuanfei Wang +3

Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKB…

cs.LG202122 cited

Scalable Rule-Based Representation Learning for Interpretable Classification

Zhuo Wang, Wei Zhang, Ning Liu +1

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…

cs.CL202112 cited

Entity Linking Meets Deep Learning: Techniques and Solutions

Wei Shen, Yuhan Li, Yinan Liu +3

Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields…

cs.IR20211 cited

Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation

Zeyuan Chen, Wei Zhang, Junchi Yan +2

Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynam…

cs.IR2021

Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories

Wei Zhang, Xin Lai, Jianyong Wang

In this paper, we investigate the problem of social link inference in a target Location-aware Social Network (LSN), which aims at predicting the unobserved links between users with…

cs.CL20211 cited

Graph-Based Tri-Attention Network for Answer Ranking in CQA

Wei Zhang, Zeyuan Chen, Chao Dong +3

In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstr…