Papers by Prashant Sharma
PLOD: An Abbreviation Detection Dataset for Scientific Documents (2022.lrec-1)
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| Challenge: | Existing datasets for abbreviation detection and extraction are limited. |
| Approach: | They propose to use a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbrevian and long forms. |
| Outcome: | The proposed dataset has an F1 score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. |
Cognition-aware Cognate Detection (2021.eacl-main)
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Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | Existing approaches to cognate detection use orthographic, phonetic and semantic similarity based features sets. |
| Approach: | They propose a method for enriching feature sets with cognitive features extracted from gaze behaviour data from human readers’ gaze behaviour. |
| Outcome: | The proposed method improves cognate detection performance by 10% and 12% over existing methods. |