Papers with classification approaches
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets (2025.acl-srw)
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| Challenge: | Recent advances in NLP have enabled the use of text-to-text annotation without providing training samples. |
| Approach: | They propose a text-to-text interface for automatic annotation using written guidelines without providing training samples. |
| Outcome: | The proposed approach is comparable with the fine-tuned BERT but without any training data. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
Ranking-Constrained Learning with Rationales for Text Classification (2022.findings-acl)
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| Challenge: | Existing approaches to text classification use labels and rationales as ranking constraints. |
| Approach: | They propose a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints to speed up deep learning models with limited training data. |
| Outcome: | The proposed approach outperforms baselines on three human-annotated datasets and shows that it is more efficient than existing approaches. |
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods to extract training instances from unlabeled texts are expensive . sentences that contain the target relations in texts can be scarce and difficult to find . |
| Approach: | They propose a framework that can automatically extract training instances from unlabeled texts for RE. |
| Outcome: | The proposed method can extract training instances from unlabeled texts for RE. |
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification (2021.findings-emnlp)
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| Challenge: | Existing relationships between entities can be reliable indicators for classifying sensitive information, such as commercially sensitive information. |
| Approach: | They propose to represent entities and relations within a single embedding to better capture the relationship between the entities. |
| Outcome: | The proposed method significantly improves the effectiveness of sensitivity classification compared to existing methods. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |
A Dataset for Multi-lingual Epidemiological Event Extraction (2020.lrec-1)
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| Challenge: | Using the Web, we propose a corpus for information extraction and text classification. |
| Approach: | They propose to use a corpus for information extraction and natural language processing (NLP) tasks such as text classification. |
| Outcome: | The proposed corpus can be used for information extraction and natural language processing tasks such as text classification. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |