The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)
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| Challenge: | a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents. |
| Approach: | They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification . |
| Outcome: | The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level. |
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| Challenge: | Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm . |
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| Challenge: | Existing methods for topic labeling use weak labelers to train rankers . recent studies show that weakly-supervised methods can produce meaningful labels . |
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| Challenge: | Existing methods for annotating data are limited by ambiguity and lack of context in data samples. |
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| Challenge: | Weak supervision is a problem in text classification, but it requires corpusspecific knowledge. |
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Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
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| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
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META: Metadata-Empowered Weak Supervision for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods for weakly supervised text classification use text data alone to generate pseudo-labels . strong label indicators exist in metadata and it has been long overlooked due to challenges . |
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Hierarchical Label Generation for Text Classification (2023.findings-eacl)
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
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