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Qingkai Zeng

University of Notre Dame

5 papers hereh-index 12669 citations28 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.SE1
affiliations
  • University of Notre Dame
Homepage
same name
  • Qingkai Zeng — 18 papers, h 10
  • Qingkai Zeng — 4 papers, h 4
  • Qingkai Zeng — 3 papers, h 3
  • Qingkai Zeng — 3 papers, h 2
  • Qingkai Zeng — 2 papers

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

most citedEnhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

38 citations · 46 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2021★ 38 cited

Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

Qingkai Zeng, Jinfeng Lin, Wenhao Yu +2

Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new…

cs.CL2021

Validating Label Consistency in NER Data Annotation

Qingkai Zeng, Mengxia Yu, Wenhao Yu +2

Data annotation plays a crucial role in ensuring your named entity recognition (NER) projects are trained with the right information to learn from. Producing the most accurate labe…

cs.CL2020★ 1 cited

Crossing Variational Autoencoders for Answer Retrieval

Wenhao Yu, Lingfei Wu, Qingkai Zeng +3

Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Questio…

cs.CL2020

Enhancing Factual Consistency of Abstractive Summarization

Chenguang Zhu, William Hinthorn, Ruochen Xu +4

Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applic…

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