Papers with NLP algorithms
Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation (2024.findings-acl)
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Brooklyn Sheppard, Anna Richter, Allison Cohen, Elizabeth Smith, Tamara Kneese, Carolyne Pelletier, Ioana Baldini, Yue Dong
| Challenge: | the Biasly dataset captures misogyny in movies in ways unique within the literature. |
| Approach: | The Biasly dataset captures misogyny in North American film by combining annotations of movie subtitles with common NLP algorithms. |
| Outcome: | The Biasly dataset captures misogyny expressions in North American film . it contains annotations of movie subtitles and text generation for rewrites . |
Time-and-Space-Efficient Weighted Deduction (2023.tacl-1)
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| Challenge: | Unweighted deduction allows a generic forward-chaining execution strategy, but weighted deduction requires a constant factor more time and space. |
| Approach: | They propose a generic unweighted deduction strategy that uses a factor more time and space than unweighting deduction . they also propose an extension to cyclic deduction systems based on Tarjan . |
| Outcome: | The proposed method is based on the proposed method and is compared with cyclic deduction systems. |
Deconfounded Lexicon Induction for Interpretable Social Science (N18-1)
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| Challenge: | Lexical features are useful beyond predictive performance. they can also be used to understand the subjective properties of a text. |
| Approach: | They propose two deep learning algorithms that separate the explanatory power of text from confounds. |
| Outcome: | The proposed algorithms are predictive of a set of target variables yet uncorrelated to confounds . they pick words associated with narrative persuasion and are more predictive than standard features . |