Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore dataset contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore corpus (raw) contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
280 decisions were annotated by one trained annotator and then manually adjudicated by one trained curator. 70 decisions were annotated by two trained annotators and then manually adjudicated by one trained curator. Adjudication was conducted destructively, therefore dataset contains only the correct annotations and does not contain all original annotations.
Corpus was developed as training and testing material for text segmentation tasks. Dataset contains decision segmented into Header, Procedural History, Submission/Rejoinder, Court Argumentation, Footer, Footnotes, and Dissenting Opinion. Segmentation allows to treat different parts of text differently even if it contains similar linguistic or other features.
The database contains annotated reflective sentences, which fall into the categories of reflective writing according to Ullmann's (2019) model. The dataset is ready to replicate these categories' prediction using machine learning. Available from: https://anonymous.4open.science/repository/c856595c-dfc2-48d7-aa3d-0ccc2648c4dc/data
The aim of the course is to introduce digital humanities and to describe various aspects of digital content processing.
The course consists of 10 lessons with video material and a PowerPoint presentation with the same content.
Every lesson contains a practical session – either a Jupyter Notebook to work in Python or a text file with a short description of the task. Most of the practical tasks consist of running the programme and analyse the results.
Although the course does not focus on programming, the code can be reused easily in individual projects.
Some experience in running Python code is desirable but not required.