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researcher

Ming Zhang

4 papers hereh-index 3813.1k citations144 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.LG1
  • cs.SI1
same name
  • Ming Zhang — 4 papers
  • Ming Zhang — 3 papers
  • Ming Zhang — 2 papers, h 5
  • Ming Zhang — 2 papers
  • Ming Zhang — 2 papers
  • Ming Zhang — 1 paper, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152017
most citedLINE: Large-scale Information Network Embedding

4.8k citations · 4.8k across the 2 of their papers we have counts for

collaborators

4 papers

cs.SI2017★ 27 cited

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Meng Qu, Jian Tang, Jingbo Shang +3

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches u…

cs.CL2016

Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction

Yichun Yin, Furu Wei, Li Dong +3

In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is…

cs.CL2016

StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation

Xiang Li, Lili Mou, Rui Yan +1

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presum…

cs.LG2015★ 4.8k cited

LINE: Large-scale Information Network Embedding

Jian Tang, Meng Qu, Mingzhe Wang +3

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classifica…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.