PDiT 2.0 is a new version of the Prague Discourse Treebank. It contains a complex annotation of discourse phenomena enriched by the annotation of secondary connectives.
The Prague Discourse Treebank 3.0 (PDiT 3.0) is a new version of annotation of discourse relations marked by primary and secondary discourse connectives in the data of the Prague Dependency Treebank. With respect to the previous versions, PDiT 3.0 brings a largely revised annotation of discourse relations and offers the data also in the Penn Discourse Treebank 3.0 (PDTB 3.0) format and sense taxonomy.
Preamble 1.0 is a multilingual annotated corpus of the preamble of the EU REGULATION 2020/2092 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. The corpus consists of four language versions of the preamble (Czech, English, French, Polish), each of them annotated with sentence subjects.
The data were annotated in the Brat tool (https://brat.nlplab.org/) and are distributed in the Brat native format, i.e. each annotated preamble is represented by the original plain text and a stand-off annotation file.
The dataset used for the Ptakopět experiment on outbound machine translation. It consists of screenshots of web forms with user queries entered. The queries are available also in a text form. The dataset comprises two language versions: English and Czech. Whereas the English version has been fully post-processed (screenshots cropped, queries within the screenshots highlighted, dataset split based on its quality etc.), the Czech version is raw as it was collected by the annotators.
Restaurant Reviews CZ ABSA - 2.15k reviews with their related target and category
The work done is described in the paper: https://doi.org/10.13053/CyS-20-3-2469
The data contains the morphemic dictionary scanned in the PDF format. It is divided into 3 parts:
introductions.pdf - pp. 11-102
main_dictionary.pdf - pp. 113-506
appendices.pdf - pp. 509-645
The file contains all Czech verbs included in the Retrograde Morphemic Dictionary of Czech Language (Slavíčková Eleonora, Academia 1975).
The data was obtained by scanning a portion of the dictionary that contains words ending in -ci and -ti. Among them, there were 18 non-verbs, which were removed. Using OCR, the data was converted into the plain text format and the result was checked by two independent readers. However, if a user encounters a forgotten error, please report.
RobeCzech is a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-theart results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base, both for PyTorch and TensorFlow.
The item contains a list of 2,058 noun/verb conversion pairs along with related formations (word-formation paradigms) provided with linguistic features, including semantic categories that characterize semantic relations between the noun and the verb in each conversion pair. Semantic categories were assigned manually by two human annotators based on a set of sentences containing the noun and the verb from individual conversion pairs. In addition to the list of paradigms, the item contains a set of 739 files (a separate file for each conversion pair) annotated by the annotators in parallel and a set of 2,058 files containing the final annotation, which is included in the list of paradigms.
Sentiment analysis models for Czech language. Models are three Czech sentiment analysis datasets(http://liks.fav.zcu.cz/sentiment/): Mall, CSFD, Facebook, and joint data from all three datasets above, using Czech version of BERT model, RobeCzech.
We present the best model for every dataset. Mall and CSFD models are new state-of-the-art for respective data.
Demo jupyter notebook is available on the project GitHub.
These models are a part of Czech NLP with Contextualized Embeddings master thesis.