LEARNING THROUGH DISAGREEMENTS IN TEXT CLASSIFICATION: ANNOTATOR WEIGHTING AND LARGE LANGUAGE MODEL ASSISTED PREDICTION
The progress in Natural Language Processing (NLP) has brought about challenges in managing disagreements within annotated datasets, particularly in text classification tasks. This final project explores innovative methods to tackle annotation discrepancies by employing multi-annotator modeling and p...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87586 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |