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20232025
most citedCMNER: A Chinese Multimodal NER Dataset based on Social Media

1 citations · 2 across the 9 of their papers we have counts for

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Showing 2024 · cs.CLShow all

6 papers · 2 filters

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

Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing

Chengjie Zhou, Bobo Li, Hao Fei +3

Structured Sentiment Analysis (SSA) was cast as a problem of bi-lexical dependency graph parsing by prior studies. Multiple formulations have been proposed to construct the graph,…

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

Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

Minghui Xu, Hao Fei, Fei Li +4

Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which…

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…