Papers with NLP analysis
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts (2021.findings-emnlp)
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| Challenge: | a large amount of work is required to clean digitized books for NLP analysis because of errors in the scanned text and duplicate volumes in the corpora. |
| Approach: | They propose methods to handle optical character recognition errors in scanned texts . they identify the canonical version for each of 17,136 repeatedly-scanned books . |
| Outcome: | The proposed method corrects over six times as many errors as it introduces, the authors show . the authors evaluate a collection of 19,347 texts from the Gutenberg dataset and 96,635 from the HathiTrust Library . |
GENTRAC: A Tool for Tracing Trauma in Genocide and Mass Atrocity Court Transcripts (2024.lrec-main)
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| Challenge: | GENTRAC analyzes witness statements of genocide and mass atrocity trials using a sophisticated parsing algorithm and a powerful tool for detecting trauma. |
| Approach: | They propose to use a web-based tool to analyze potentially traumatic content in witness statements of genocide and mass atrocity trials. |
| Outcome: | The tool visualizes the density of such content throughout a trial day and provides statistics on the overall amount of traumatic content and speaker distribution. |
LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP (2024.acl-long)
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| Challenge: | Existing pipelines for natural language processing only process symbolic representations of language, which are labor-intensive and noisy . a large portion of logographic data persists in a purely visual form due to the absence of transcription . this issue poses a bottleneck for researchers seeking to apply NLP to ancient logographic languages . |
| Approach: | They propose a benchmark for NLP analysis of ancient logographic languages using visual representations of writing. |
| Outcome: | The proposed pipeline outperforms existing pipelines for some tasks . the results could unlock large amounts of cultural heritage data of ancient logographic languages . |