Papers with Entity Extraction
Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)
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| Challenge: | Named Entity Recognition (NER) is a subtask of Information Extraction in NLP. |
| Approach: | They present a Telugu-English code-mixed corpus with the corresponding named entity tags. |
| Outcome: | The proposed model scored 0.96, 0.94 and 0.95 on a Telugu-English code-mixed corpus. |
Lightweight Domain-Specific Language Model for Real-Time Structuring of Medical Prescriptions (2026.eacl-industry)
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| Challenge: | Existing language models ignore layout information, rely on expensive image-based architectures, or cannot operate under privacy and hardware constraints. |
| Approach: | They propose a lightweight, privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in french medical prescriptions. |
| Outcome: | The proposed model matches or surpasses larger document-understanding models on strict extraction metrics while maintaining essential spatial cues. |
SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking (2023.findings-emnlp)
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| Challenge: | SWEET is a weak supervision pipeline for extracting person names from noisy escort ads . it does not require any human annotators and labeling, which is incredibly important . |
| Approach: | They propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting person names from noisy escort ads. |
| Outcome: | The proposed weak supervision pipeline outperforms the previous method by 9% on domain data and generalizes to common benchmark datasets. |
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)
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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng
| Challenge: | Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency. |
| Approach: | They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment . |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples. |