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Publication Year: 2026

1 to 10 of 1,336 Results

Interview protocol.docx Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026) MS Word - 2.9 MB - MD5: 3ac5ee1f4d77bbdcdbcc9a455653baeb Interview protocol
readme.docx Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026) MS Word - 17.6 KB - MD5: 947c8bd11039fe671e72e53fbbca4dc4 Readme file
Thematic Analysis.tab Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026) Tabular Data - 5.4 KB - 30 Variables, 37 Observations - UNF:6:L5b6urQGjgdncMxbgvZvcg== thematic analysis - results
00_navez_gold_standard_preprocessing_json2df.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 465.1 KB - MD5: 6062b90f470e79ad4062aa678de087d3 Preprocessing notebook that converts Label Studio JSON exports into the structured gold-standard corpus used throughout the experiments. The workflow merges annotation projects, extracts entity annotations, aligns character offsets, generates IOB labels, removes unsupported neste...
01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 201.0 KB - MD5: fafce906de349b467636197fa64a4d95 Implements Workflow 1 described in the article by evaluating the off-the-shelf spaCy fr_core_news_sm model using only the isolated Named Entity Recognition (NER) component. The notebook evaluates the model on the held-out test set without additional domain-specific training.
01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 248.3 KB - MD5: 4d33ecdf3831757bac0319676a57bb2a Implements Workflow 1 using the larger fr_core_news_lg model. The notebook evaluates the isolated NER component on the historical correspondence corpus and compares its performance with the smaller spaCy model.
02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 200.0 KB - MD5: 2998c19d3448da4d9ed7a7776de6e9dc Implements Workflow 2 by using the complete spaCy fr_core_news_sm pipeline, including all NLP components. The notebook assesses whether embedding the NER component within the full pipeline influences recognition performance.
02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 255.7 KB - MD5: 1e35b33c8202285ec93bb0d8508ec8ff Implements Workflow 2 using the complete fr_core_news_lg pipeline. Results are compared with Workflow 1 to evaluate the effect of the full NLP pipeline on NER performance.
03_navez_NER_spacy_custom-trained_dk.ipynb Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Jupyter Notebook - 575.5 KB - MD5: 5b3ef0c547261ba0b3323ce0fd2ca50b Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...
best_hps.json Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal JSON - 474 B - MD5: a34a656006d26bd49168cb552f29955f JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.

Interview protocol.docx

Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026)

MS Word - 2.9 MB -

Interview protocol

readme.docx

Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026)

MS Word - 17.6 KB -

Readme file

Thematic Analysis.tab

Sep 4, 2026 - Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026)

Tabular Data - 5.4 KB - 30 Variables, 37 Observations -

thematic analysis - results

00_navez_gold_standard_preprocessing_json2df.ipynb

Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal

Jupyter Notebook - 465.1 KB -

Preprocessing notebook that converts Label Studio JSON exports into the structured gold-standard corpus used throughout the experiments. The workflow merges annotation projects, extracts entity annotations, aligns character offsets, generates IOB labels, removes unsupported neste...

01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.ipynb

Jupyter Notebook - 201.0 KB -

Implements Workflow 1 described in the article by evaluating the off-the-shelf spaCy fr_core_news_sm model using only the isolated Named Entity Recognition (NER) component. The notebook evaluates the model on the held-out test set without additional domain-specific training.

01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.ipynb

Jupyter Notebook - 248.3 KB -

Implements Workflow 1 using the larger fr_core_news_lg model. The notebook evaluates the isolated NER component on the historical correspondence corpus and compares its performance with the smaller spaCy model.

02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.ipynb

Jupyter Notebook - 200.0 KB -

Implements Workflow 2 by using the complete spaCy fr_core_news_sm pipeline, including all NLP components. The notebook assesses whether embedding the NER component within the full pipeline influences recognition performance.

02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.ipynb

Jupyter Notebook - 255.7 KB -

Implements Workflow 2 using the complete fr_core_news_lg pipeline. Results are compared with Workflow 1 to evaluate the effect of the full NLP pipeline on NER performance.

03_navez_NER_spacy_custom-trained_dk.ipynb

Jupyter Notebook - 575.5 KB -

Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...

best_hps.json

JSON - 474 B -

JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.

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