Zero shot classification for unstructured text of archival value

english

2024

volume 34, issue 1

classification   description   machine learning   unstructured text   zero shot classi-fication

Mednarodni inštitut arhivskih znanosti Trst-Maribor
(Obvezni izvod spletne publikacije)

Purpose: The purpose of the article is to investigate if artificial intelligence and, subsequently, machine learning can provide any solutions to ease some of the archival tasks when dealing with classification of unstructured texts which have archival value. The research was aimed specifically on how to approach a specif-ic archival task within content classification of unstructured texts.Method/approach: In the research, the methods of content analysis and experi-ment were used. Different approaches to managing the classification of unstruc-tured text with the use of machine learning were investigated, as well as the conduction of experiment testing of some of the most prominent technological solutions currently available. Results: The research showed that the use of machine learning for the purpose of classification in managing unstructured text with archival value is achievable and effective.Conclusion: The approach, with its method and technology, which was used in the research is mature, manageable, and available to carry out the archival task of classification of unstructured text where needed. Zero shot classification pro-vides a suitable path to solve problems relating to the classification of unstruc-tured texts of archival value where pre-labelled data for following the supervised approach to create the model for classification is not available.

20.12.2024