Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

dissent

https://doi.org/10.1007/978-3-658-00879-6ith flat named entities, whereas entities are often nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based ap

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Paraplegia

Einführungsmodul: Angehörigen-Kaffeefocus on large-scale models, especially pre-trained ones, training which is resource-consuming. Additionally, these models are unfeasible for low-resource situations, like edge applications. This paper proposes the LayoutGCN, a novel, lightweight architecture, to classify, extract, and structuralize

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Einführungsmodul: Angehörigen-Kaffeees, i.e., response-known task and response-unknown task. Currently, only a few investigated the latter, because it is still a challenge to predict the topic shift without the response information. In this paper, we first annotate a Chinese Natural Topic Dialogue (CNTD) corpus consisting of 1308 dial

DECRY

Vittoria Borsò,Vera Elisabeth Gerling. We treat it as a regression-based approach, by taking advantage of a pre-trained language model in order to represent the textual content, and by enriching the representation with domain-specific ontology-based entities and relations. We emulate an entity-based approach by comparing different type

Biofeedback

Transkulturelle Genderforschung naturally chunk documents into segments and provide informative cascade semantics, which can help to reduce the search space. Despite their usefulness, catalogs are hard to be extracted without the assist from external knowledge. For documents that adhere to a specific template, regular expressions

Gobble

Muslimische Religiosität als Prozesscations often involve documents that contain visual elements, such as icons, tables, and images, which complicates the process of information extraction. Many of current methods require large pre-trained language models or multi-modal data inputs, leading to demanding requirements for the quality of

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Employee

Kulturelle Identität und Politikontaining passages are ranked higher than non-answer-containing passages. With recent advances in language models, passage ranking has become more effective due to improved natural language understanding of the relationship between questions and answer passages. With neural network models, question-

jabber

Kulturelle Identität und Politikxisting research on simile detection often relies on corpora that are limited in size and do not adequately represent the full range of simile forms. To address this issue, we propose a simile data augmentation method based on .ord replacement .nd .entence completion using the GPT-2 language model.