Papers by Abhishek Arora
Contrastive Entity Coreference and Disambiguation for Historical Texts (2024.emnlp-main)
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| Challenge: | Existing methods for disambiguating historical documents are not accurate for historical documents, which are replete with individuals not remembered in contemporary knowledge bases. |
| Approach: | They propose to train bi-encoder models for coreferencing and disambiguating individuals in historical texts and evaluate them on a historical newswire benchmark. |
| Outcome: | The proposed model outperforms existing models on the historical newswire benchmark and on other datasets. |
EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge (2023.emnlp-demo)
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| Challenge: | Existing OCR engines fail to provide accurate, cost-effective and sample-efficient character recognition for public domain documents. |
| Approach: | EffOCR is an open-source optical character recognition package that is accurate, cheap to deploy and sample efficient to customize to novel collections, languages, and character sets. |
| Outcome: | EffOCR model trains character retrieval problem and scales to novel collections, languages, and character sets. |
Do Transformers Parse while Predicting the Masked Word? (2023.emnlp-main)
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| Challenge: | Existing studies show that pre-trained language models encode linguistic structures like parse trees while being trained unsupervised. |
| Approach: | They propose to train pre-trained language models to encode linguistic structures like parse trees while unsupervised. |
| Outcome: | The proposed model performs optimally for masked language modeling loss on the English PCFG. |
LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models (2024.acl-demos)
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| Challenge: | Large language models (LLMs) are used for many computational analyses, but approximate string matching packages are not widely used in social science applications. |
| Approach: | The open-source package LinkTransformer provides an end-to-end software for performing record linkage and other data cleaning tasks with transformer LLMs. |
| Outcome: | The open-source package LinkTransformer outperforms standard methods in a variety of languages and settings. |
Quantifying Character Similarity with Vision Transformers (2023.emnlp-main)
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| Challenge: | Off-the-shelf string matching methods are widely used to link entities across datasets, but they are not available for many settings. |
| Approach: | They propose to use augmented digital fonts to measure character substitution costs for OCR’ed documents by using vision transformers. |
| Outcome: | The proposed method significantly improves record linkage compared to other string matching methods. |