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Post-correction of Historical Text Transcripts with Large Language Models: An Exploratory Study

Publications associées (26)

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of ...

Springer International Publishing2020

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an extended overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and ...

CEUR-WS2020

Fast Transformers with Clustered Attention

François Fleuret, Angelos Katharopoulos, Apoorv Vyas

Transformers have been proven a successful model for a variety of tasks in sequence modeling. However, computing the attention matrix, which is their key component, has quadratic complexity with respect to the sequence length, thus making them prohibitivel ...

2020

A Complexity Analysis Approach for Model-based System Engineering

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng, Jinwei Chen

With the increasing complexity of the highly engineered products, Model-based Systems Engineering (MBSE) is proposed to support the complexity management of the product development. As the basic of complexity management, complexity analysis is used to meas ...

IEEE2020

On the Error Rate of the LoRa Modulation With Interference

Andreas Peter Burg, Matthieu Cotting, Alexios Konstantinos Balatsoukas Stimming, Orion Afisiadis

LoRa is a chirp spread-spectrum modulation developed for the Internet of Things (IoT). In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Enno Hermann

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...

2021