Papers with Micro-F1
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (2020.coling-main)
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| Challenge: | Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. |
| Approach: | They propose a memory-augmented FNET model to tackle unseen types in a zero-shot manner. |
| Outcome: | The proposed model outperforms the state-of-the-art models with up to 8% gain in Micro-F1 and Macro-F1. |
What’s wrong with your model? A Quantitative Analysis of Relation Classification (2024.starsem-1)
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| Challenge: | A major trend in NLP research aims at designing more sophisticated setups to improve the state-of-the-art (SOTA) on a target task. |
| Approach: | They propose an in-depth analysis suite for Relation Classification to be used for prediction tasks. |
| Outcome: | The proposed model improves over the baseline by >3 Micro-F1 . the proposed model is based on a case study and a preliminary error-guided analysis . |
Explainable Prediction of Medical Codes from Clinical Text (N18-1)
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| Challenge: | Clinical notes are text documents that are created by clinicians for each patient encounter. |
| Approach: | They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. |
| Outcome: | The proposed method is accurate and better than the current state of the art. |
Structured Confidence–Guided Online Adaptation for LLM-based Multi-Label Classification (2026.findings-acl)
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| Challenge: | Large language models (LLMs) enable zero-shot and few-shot multi-label text classification . but most approaches perform static inference and degrade under streaming test data . |
| Approach: | They propose a structured confidence-guided online adaptation framework for LLM-based multi-label generation without parameter updates. |
| Outcome: | The proposed framework improves Micro-F1 and Macro-F1, with the largest gains on long-tail labels. |
Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification (2025.findings-acl)
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| Challenge: | Existing approaches to multi-label text classification are limited by textual data. |
| Approach: | They propose a domain-agnostic generative model framework for multi-label text classification that generates predefined label descriptions and matches them to predefined labels. |
| Outcome: | The proposed model achieves 13.94% and 24.85% performance over all datasets. |