Papers with preprocessing pipelines
The D-WISE Tool Suite: Multi-Modal Machine-Learning-Powered Tools Supporting and Enhancing Digital Discourse Analysis (2023.acl-demo)
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| Challenge: | The D-WISE Tool Suite addresses limitations of current DH tools due to the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which discourses of contemporary societies are encoded. |
| Approach: | They propose to use D-WISE Tool Suite to analyze heterogeneous, unstructured, and multi-modal data in the Digital Humanities (DH) |
| Outcome: | The proposed tool leverages state-of-the-art machine learning technologies from Natural Language Processing and Com-puter Vision to ensure its usability for modernDH research. |
uniblock: Scoring and Filtering Corpus with Unicode Block Information (D19-1)
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| Challenge: | Existing methods to remove sentences consisting of illegal characters are tedious and repetitive. |
| Approach: | They propose a statistical method to identify illegal characters in natural language processing . they use a fixed-size feature vector to generate a Gaussian mixture model for each sentence . |
| Outcome: | The proposed method can score sentences and filter corpus on clean corpus and improve performance. |
LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers (2025.findings-acl)
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Shantanu Ghosh, Rayan Syed, Chenyu Wang, Vaibhav Choudhary, Binxu Li, Clare B Poynton, Shyam Visweswaran, Kayhan Batmanghelich
| Challenge: | Current slice discovery methods in computer vision rely on converting input images into sets of attributes and testing hypotheses about configurations of pre-computed attributes associated with elevated error patterns. |
| Approach: | They propose a method to identify systematic biases in the mistakes of pre-trained vision models by converting input images into sets of attributes and testing hypotheses about configurations of these attributes. |
| Outcome: | The proposed method outperforms existing methods on 3 natural and 3 medical imaging datasets and generates pseudo-labels for each identified bias. |