Papers by Sharifah Mahani Aljunied
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
| Outcome: | The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively. |
Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)
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| Challenge: | Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED. |
| Approach: | They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED. |
| Outcome: | The proposed dataset improves on the existing DocRED dataset by 13 F1 points. |
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)
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| Challenge: | Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location. |
| Approach: | They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers. |
| Outcome: | The proposed model outperforms baselines and reveal possible directions for future research. |