Document exploration in archives is often challenging due to the lack of organization in topic-based categories. Moreover, archival records only provide short text which is often insufficient for capturing the semantic. This paper proposes and explores a dataless categorization approach that utilizes word embeddings and TF-IDF to categorize archival documents. Additionally, it introduces a visual approach built on top of the word embeddings to enhance the exploration of data. Preliminary results suggest that current vector representations alone do not provide enough external knowledge to solve this task.

The challenges of German archival document categorization on insufficient labeled data

Danilo Dessi';
2020

Abstract

Document exploration in archives is often challenging due to the lack of organization in topic-based categories. Moreover, archival records only provide short text which is often insufficient for capturing the semantic. This paper proposes and explores a dataless categorization approach that utilizes word embeddings and TF-IDF to categorize archival documents. Additionally, it introduces a visual approach built on top of the word embeddings to enhance the exploration of data. Preliminary results suggest that current vector representations alone do not provide enough external knowledge to solve this task.
Cultural Heritage; Dataless Categorization; Document Exploration; Text Categorization
File in questo prodotto:
File Dimensione Formato  
2020 - The Challenges of German Archival Document Categorization on Insufficient Labeled Data.pdf

accesso aperto

Tipologia: versione editoriale
Dimensione 259.07 kB
Formato Adobe PDF
259.07 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/321829
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
social impact