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Jun Zhou

4 papers here

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

fields
  • cs.CL4
same name
  • Jun Zhou — 28 papers, h 26
  • Jun Zhou — 22 papers, h 10
  • Jun Zhou — 21 papers, h 20
  • Jun Zhou — 17 papers, h 7
  • Jun Zhou — 16 papers, h 22
  • Jun Zhou — 15 papers, h 5

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 citedCMNER: A Chinese Multimodal NER Dataset based on Social Media

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2024

Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding

Jun Zhou, Dongyang Yu, Kamran Aziz +4

Fine-grained sentiment analysis involves extracting and organizing sentiment elements from textual data. However, existing approaches often overlook issues of category semantic inc…

cs.CL2024

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm

Qiang Gao, Zixiang Meng, Bobo Li +4

Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the rol…

cs.CL2024

Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information

Qiang Gao, Bobo Li, Zixiang Meng +5

Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such…

cs.CL2024★ 1 cited

CMNER: A Chinese Multimodal NER Dataset based on Social Media

Yuanze Ji, Bobo Li, Jun Zhou +3

Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, a notable paucity of da…

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