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researcher

Wei Fan

17 papers hereh-index 324.8k citations74 works total

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

author position
  • middle author13
  • last author3

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.AI4
  • cs.DB3
  • cs.CV2
same name
  • Wei Fan — 19 papers, h 15
  • Wei Fan — 19 papers, h 8
  • Wei Fan — 11 papers, h 8
  • Wei Fan — 8 papers, h 8
  • Wei Fan — 7 papers, h 7
  • Wei Fan — 6 papers, h 1

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
20152026
most citedMulti-Grained Named Entity Recognition

98 citations · 193 across the 17 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series

Frank Nie, Ethan B Liu, Yuan Zhu +2

Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications. Although recent multimodal time-series large lan…

cs.AI2020★ 6 cited

Commonsense Evidence Generation and Injection in Reading Comprehension

Ye Liu, Tao Yang, Zeyu You +2

Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond. To empower the machine with commonsense reasoning, in this p…

cs.AI2018★ 2 cited

On the Generation of Medical Question-Answer Pairs

Sheng Shen, Yaliang Li, Nan Du +7

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the req…

cs.AI2018★ 5 cited

Finding Similar Medical Questions from Question Answering Websites

Yaliang Li, Liuyi Yao, Nan Du +5

The past few years have witnessed the flourishing of crowdsourced medical question answering (Q&A) websites. Patients who have medical information demands tend to post questions ab…

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