activity
20152022
most citedEffective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

13 citations · 16 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2022

Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering

Jun Gao, Wei Wang, Changlong Yu +3

Representations of events described in text are important for various tasks. In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering fra…

cs.CL2020

When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models

Changlong Yu, Jialong Han, Peifeng Wang +4

We address hypernymy detection, i.e., whether an is-a relationship exists between words (x, y), with the help of large textual corpora. Most conventional approaches to this task ha…

cs.CL20202 cited

Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations

Changlong Yu, Hongming Zhang, Yangqiu Song +2

Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities. In this…

cs.CL20201 cited

Multiplex Word Embeddings for Selectional Preference Acquisition

Hongming Zhang, Jiaxin Bai, Yan Song +5

Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such repr…

cs.IR2019

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

Fuyu Lv, Taiwei Jin, Changlong Yu +4

Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching appr…

cs.DC201513 cited

Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

Da Yan, James Cheng, Yi Lu +1

Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing syst…