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This repository is part of an NLP course for humanities and cultural studies. This course uses historical newspapers as a source and applies NLP methods to them. NLP tasks: Tokenization, Lemmatization, TF-IDF, Part-of-speech tagging, semantic search with transformers, article extraction and OCR post-correction with LLMs, NER and text classification

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Anleitungen.html
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Data_Detective_NLP_Introduction.ipynb
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Download_über_API_der_DDB.ipynb
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Glossary.html
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Large_Language_Models_Article_Separation.ipynb
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Named_Entity_Recognition_ImpressoAPI.ipynb
Named_Entity_Recognition_ImpressoAPI.ipynb
 
 
Prompting_Techniques_LLMs.ipynb
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Python_CrashCourse_1.ipynb
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Python_CrashCourse_2.ipynb
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QA_based_Entity_Extraction.ipynb
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README.md
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Text_Classification.ipynb
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Transformers_SemantischSearch.ipynb
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Created by Sarah Oberbichler ORCID

NLP Course for Digital Humanities and Cultural Studies

Welcome to the repository of the NLP course for Digital Methods in the Humanities

About the Course

This course offers an introduction to Natural Language Processing (NLP) and its application in digital humanities. The course is part of the Master's program "Digital Methods for Humanities and Cultural Studies (DMGK)" in Mainz.

Course Website: https://ieg-dhr.github.io/NLP-Course4Humanities_2024/

Course Contents

The course covers the following topics:

  • Introduction to NLP, Jupyter Notebooks, and Python
  • Using SpaCy, SKLEARN and NLTK for NLP tasks
  • German Newspaper Portal and its API
  • Transformer models for semantic search and text similarity (Word Embeddings)
  • Large Language Models (LLMs) for Semantic Text Extraction (Article Extraction) and Post-OCR Correction
  • Named Entity Recognition (NER) and Text Classification

Repository Structure

  • index.html: Main page of the course
  • styles.css: CSS stylesheet for the course website
  • datasets/: Folder for course materials and resources

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About

This repository is part of an NLP course for humanities and cultural studies. This course uses historical newspapers as a source and applies NLP methods to them. NLP tasks: Tokenization, Lemmatization, TF-IDF, Part-of-speech tagging, semantic search with transformers, article extraction and OCR post-correction with LLMs, NER and text classification

Topics

nlp webpage text-classification teaching ner semantic-search nlp-machine-learning university-course historical-newspapers transformers-models llms article-extraction

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