Annif: DIY automated subject indexing using multiple algorithms

Manually indexing documents for subject-based access is a labour-intensive process. We propose using metadata gathered from bibliographic databases to train algorithms that assist librarians in that work. We have developed Annif, an open source tool and microservice for automated subject indexing. A...

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Vydáno v:LIBER quarterly Ročník 29; číslo 1; s. 1 - 25
Hlavní autor: Suominen, Osma
Médium: Journal Article
Jazyk:angličtina
Vydáno: Utrecht Igitur, Utrecht Publishing & Archiving Services 29.07.2019
openjournals.nl
Témata:
Algorithms
automated subject indexing
Automation
Bibliographic data bases
Classification
Copyright
Finnish language
Indexing
Information sources
Internet
Librarians
Local media
Machine learning
Metadata
Natural language processing
Public domain
Subject headings
Subject indexing
Vocabulary
ISSN:2213-056X, 2213-056X
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Popis
Shrnutí:Manually indexing documents for subject-based access is a labour-intensive process. We propose using metadata gathered from bibliographic databases to train algorithms that assist librarians in that work. We have developed Annif, an open source tool and microservice for automated subject indexing. After training it with a subject vocabulary and existing metadata, Annif can be used to assign subject headings for new documents. We have tested Annif with different document collections including scientific papers, old scanned books and contemporary e-books, Q&A pairs from an “ask a librarian” service, Finnish Wikipedia, and the archives of a local newspaper. The results of analysing scientific papers and current books have been reassuring, while other types of documents have proved to be more challenging. The current version is based on a combination of existing natural language processing and machine learning tools. By combining multiple approaches and existing open source algorithms, Annif can build on the strengths of individual algorithms and adapt to different settings. With Annif, we expect to improve subject indexing and classification processes especially for electronic documents as well as collections that otherwise would not be indexed at all.
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ISSN:2213-056X
2213-056X
DOI:10.18352/lq.10285