Digital Humanities B (Language Processing and Information Retrieval)

All Digital Humanities courses

Fall–winter 2026

Course schedule

All classes meet on Tuesdays, 14:40–16:10, in DH Lab B307. Lab computers are available.

Class Date Topic
1 October 6 Course overview, introductions, and text search
2 October 13 Classifying movement in context
3 October 20 Searching by lemma and pattern
4 October 27 Comparing authors and models
5 November 10 Checking an agent’s evidence (in preparation)

In classes 1–5, students investigate movement in 「蜘蛛の糸」 and other works as one continuing study, reported in an assignment set in class 5. Materials for class 5 onward are published before each class. There is no class on November 3.

  • Course guide: assessment, attendance, submissions, and the final project.
  • Course resources: command and classification references, terms and ideas, how-to guides, the movement worksheet, and the NER comparison.

Course purpose

This course develops practical skills in natural language processing (NLP) and information retrieval for humanities research. Using modern Japanese and English literature, students compare dictionary and rule-based methods, trained models, and generative LLMs, and evaluate their results, limitations, computational requirements, and reproducibility.

Learning goals

  • Prepare and search corpora with metadata.
  • Write basic Python text-processing programs and create and interpret visualizations.
  • Apply text search, topic models, named entity recognition (NER), and document classification.
  • Compare dictionary and rule-based methods, trained models such as GLiNER, and generative large language models (LLMs) using source texts and shared evaluation criteria.
  • Produce a reproducible analysis notebook or program, with an explanation of the research question, method, evidence, and limitations.

Sources and tools

Search the course pages