Named Entity Recognition for Cultural Heritage Preservation

Aejas, Bajeela; Bouras, Abdelaziz; Belhi, Abdelhak; Gasmi, Houssem

doi:10.1007/978-3-030-66777-1_11

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Although these methodologies were able to produce improvements with respect to the use of a KOS-based approach for the annotation of a corpus through DS, the precision of the manual annotation has not still been reached. Thus, the development of approaches able to ease and support the manual annotation can help to narrow this gap, especially in low resource languages and specific domains, such as tourism and cultural heritage (Aejas et al. , 2021).…”

Section: Related Workmentioning

confidence: 99%

Integrated use of KOS and deep learning for data set annotation in tourism domain

Aracri

Folino

Silvestri

2023

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PurposeThe purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.Design/methodology/approachA method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.FindingsThe study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.Originality/valueThe paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.

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Section: Related Workmentioning

confidence: 99%

Integrated use of KOS and deep learning for data set annotation in tourism domain

Aracri

Folino

Silvestri

2023

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“…The main techniques to extract entities from historical texts include temporal entity extraction, event extraction, and named entity recognition (NER) [25,26]. In many cases, entity extraction is performed manually [27]. In other cases, automatic techniques for entity extraction are used [28][29][30].…”

Section: Entity Extractionmentioning

confidence: 99%

Genealogical Data Mining from Historical Archives: The Case of the Jewish Community in Pisa

et al. 2023

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The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della Comunita Ebraica di Pisa (ASCEPI) project, with a focus on the extraction of data from the Nati, Morti e Ballottati (NMB) Registry document in the archive. The NMB Registry contains about 1900 records of births, deaths, and balloted individuals within the Jewish community in Pisa. The study uses a semiautomatic pipeline of digitization, transcription, and Natural Language Processing (NLP) techniques to extract personal data such as names, surnames, birth and death dates, and parental names from each record. The extracted data are then used to build a knowledge base and a genealogical tree for a representative family, Supino. This study demonstrates the potential of using NLP and rule-based techniques to extract valuable information from historical documents and to construct genealogical trees.

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“…In fact, the thrust coming from the cultural heritage sector related to ML and DL is predominantly on computer vision, than on other areas such as the one covered in this paper, i.e. NLP, that is still limited [9], [10]. In particular, the lack of adequate training datasets further invalidates the possibilities of using the latest techniques, whether supervised, semi-supervised or unsupervised.…”

Section: A Cultural Heritage Domain: Current Approachesmentioning

confidence: 99%

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

et al. 2023

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The growth of the online review phenomenon, which has expanded from specialised trade magazines to end users via online platforms, has also increasingly involved the cultural heritage of countries, a source of tourism and growth driver of local economies. Unfortunately, this has been paralleled by the emergence and spread of the phenomenon of fake reviews, against which the scientific world has developed language models capable of distinguishing them from the truthful. The application of such models, often based on deep neural networks with transformer-type architectures, is however limited by the availability of local language data sets for specific domains, useful for both training and verification. The purpose of this article is twofold. Firstly, a new data set was created in the Italian language, generally considered low-resource, relating to the domain of cultural heritage in Italy, by collecting reviews available online, reorganising them in the form of a data set usable by the language models. Secondly, a baseline of results for the detection of misleading reviews was constructed by exploiting two widely used language models, namely BERT and ELECTRA. The performance achieved is interesting, around 95% accuracy and F1 score, using data set splits between training and testing of 80/20 and 90/10. In addition, SHAP was used as a tool to support the explicability of AI models: in this way, it was possible to show the usefulness of sentiment analysis as a support for the recognition of deceptiveness.INDEX TERMS Italian cultural heritage, data set, fake reviews, sentiment analysis, deceptive.

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Named Entity Recognition for Cultural Heritage Preservation

Cited by 6 publications

References 16 publications

Integrated use of KOS and deep learning for data set annotation in tourism domain

Integrated use of KOS and deep learning for data set annotation in tourism domain

Genealogical Data Mining from Historical Archives: The Case of the Jewish Community in Pisa

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

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