Costra 1.1 is a new dataset for testing geometric properties of sentence embeddings spaces. In particular, it concentrates on examining how well sentence embeddings capture complex phenomena such paraphrases, tense or generalization. The dataset is a direct expansion of Costra 1.0, which was extended with more sentences and sentence comparisons.
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.
This corpus contains annotations of translation quality from English to Czech in seven categories on both segment- and document-level. There are 20 documents in total, each with 4 translations (evaluated by each annotator in paralel) of 8 segments (can be longer than one sentence). Apart from the evaluation, the annotators also proposed their own, improved versions of the translations.
There were 11 annotators in total, on expertise levels ranging from non-experts to professional translators.
Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.
The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.
The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.
In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.
The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.