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Sijia Wang

2 papers hereh-index 5284 citations7 works total

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

author position
  • first author2

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CV1
same name
  • Sijia Wang — 6 papers, h 6
  • Sijia Wang — 4 papers
  • Sijia Wang — 4 papers, h 2
  • Sijia Wang — 3 papers, h 3
  • Sijia Wang — 2 papers, h 0
  • Sijia Wang — 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 citedThe Art of Prompting: Event Detection based on Type Specific Prompts

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

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023

Benchmarking Diverse-Modal Entity Linking with Generative Models

Sijia Wang, Alexander Hanbo Li, Henry Zhu +9

Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality…

cs.CL2023

AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes

Barry Menglong Yao, Sijia Wang, Yu Chen +5

We propose attribute-aware multimodal entity linking, where the input consists of a mention described with a text paragraph and images, and the goal is to predict the corresponding…

cs.CL2023

RE2: Region-Aware Relation Extraction from Visually Rich Documents

Pritika Ramu, Sijia Wang, Lalla Mouatadid +2

Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout s…

cs.CL2022★ 1 cited

The Art of Prompting: Event Detection based on Type Specific Prompts

Sijia Wang, Mo Yu, Lifu Huang

We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot…

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