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Zihan Wang

University of California San Diego

16 papers hereh-index 111.3k citations22 works total

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

author position
  • first author7
  • middle author9

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CL16
affiliations
  • University of California San Diego
Homepage
same name
  • Zihan Wang — 23 papers, h 12
  • Zihan Wang — 20 papers, h 10
  • Zihan Wang — 12 papers, h 8
  • Zihan Wang — 11 papers, h 5
  • Zihan Wang — 9 papers, h 2
  • Zihan Wang — 9 papers, h 2

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
20192023
most citedCross-Lingual Ability of Multilingual BERT: An Empirical Study

171 citations · 280 across the 16 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CL2019★ 171 cited

Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Karthikeyan K, Zihan Wang, Stephen Mayhew +1

Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no ali…

cs.CL2019★ 6 cited

CrossWeigh: Training Named Entity Tagger from Imperfect Annotations

Zihan Wang, Jingbo Shang, Liyuan Liu +3

Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model compariso…

cs.CL2019★ 3 cited

Raw-to-End Name Entity Recognition in Social Media

Liyuan Liu, Zihan Wang, Jingbo Shang +5

Taking word sequences as the input, typical named entity recognition (NER) models neglect errors from pre-processing (e.g., tokenization). However, these errors can influence the m…

cs.CL2019★ 63 cited

Discriminative Topic Mining via Category-Name Guided Text Embedding

Yu Meng, Jiaxin Huang, Guangyuan Wang +4

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsuper…

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