Papers by Abhishek Arora

5 papers
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Challenge: Existing methods for disambiguating historical documents are not accurate for historical documents, which are replete with individuals not remembered in contemporary knowledge bases.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.

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