Enriching the legacy literature with OCR corrections and text-mined semantic metadata
Notice bibliographique
Résumé
The Biodiversity Heritage Library (BHL) holds the largest collection of digitised legacy literature on biodiversity. Accessible as an online, fully-featured digital library, BHL stores bibliographic metadata for digital objects, allowing its users to issue keyword-based searches over the entire collection. Furthermore, owing to the application of optical character recognition (OCR) technology on scanned items (e.g., books, monographs, journals), textual content has been made available in machine-readable form, as well as automatically linked to taxonomic names in the Encyclopedia of Life (EOL). In the work presented herein, we report on our recent efforts aimed at the further advancement of the above-mentioned BHL functionalities. In terms of content rectification, the quality of available texts is being improved through the detection and correction of OCR-generated errors using an unsupervised statistical procedure incorporated into a desktop tool. In developing this tool, we are utilising the Google Books Ngram data sets as well as the accompanying Google Ngram Viewer. We are investigating two methods for error correction: lexical distance-based and context-based approaches. The former determines the best candidate unigram given only the features of an erroneous word. Context-based correction, in contrast, takes into account a word’s surrounding context. Meanwhile, in order to extend the current BHL features with semantic search capabilities, we are employing text mining solutions to automatically extract semantic metadata that capture a wide range of concepts apart from taxa. To this end, natural language processing (NLP) pipelines have been constructed using Argo ( http://argo.nactem.ac.uk ), a Web-based, graphical text mining workbench, in order to identify other biodiversity-relevant concepts, such as expressions pertaining to people, geographic locations, habitats, morphological characteristics and time. These pipelines, i.e., workflows, are built through the straightforward combination of several analytics (e.g., gazetteers and machine learning-based concept recognisers) which have been developed specifically for the biodiversity domain. The generated semantic metadata are displayed by the workbench’s graphical user interface that allows for the validation of annotations. Argo’s support for information interoperability is two-fold: firstly, its workflows can store their results in any of a number of standard encodings, e.g., XML Metadata Interchange (XMI) and Resource Description Framework (RDF) formats. Secondly, Argo includes facilities for deploying any of its workflows as Representational State Transfer (RESTful) Web services, thus rendering our NLP tools integrable with third-party applications similarly intending to enrich free-text biodiversity resources with automatically generated semantic metadata. Finally, to facilitate exploration and understanding of the documents and metadata which will be retrieved by semantic search, appropriate information visualisations are being designed. A key aspect of this design is driven by the need to allow users to interact with the visualisations in an analytical yet intuitive manner, enabling technical and non-technical users alike to discover and access various associations amongst BHL digital objects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».