The Prague Dependency Treebank 3.5 is the 2018 edition of the core Prague Dependency Treebank (PDT). It contains all PDT annotation made at the Institute of Formal and Applied Linguistics under various projects between 1996 and 2018 on the original texts, i.e., all annotation from PDT 1.0, PDT 2.0, PDT 2.5, PDT 3.0, PDiT 1.0 and PDiT 2.0, plus corrections, new structure of basic documentation and new list of authors covering all previous editions. The Prague Dependency Treebank 3.5 (PDT 3.5) contains the same texts as the previous versions since 2.0; there are 49,431 annotated sentences (832,823 words) on all layers, from tectogrammatical annotation to syntax to morphology. There are additional annotated sentences for syntax and morphology; the totals for the lower layers of annotation are: 87,913 sentences with 1,502,976 words at the analytical layer (surface dependency syntax) and 115,844 sentences with 1,956,693 words at the morphological layer of annotation (these totals include the annotation with the higher layers annotated as well). Closely linked to the tectogrammatical layer is the annotation of sentence information structure, multiword expressions, coreference, bridging relations and discourse relations.
The Prague Dependency Treebank of Spoken Czech 2.0 (PDTSC 2.0) is a corpus of spoken language, consisting of 742,316 tokens and 73,835 sentences, representing 7,324 minutes (over 120 hours) of spontaneous dialogs. The dialogs have been recorded, transcribed and edited in several interlinked layers: audio recordings, automatic and manual transcripts and manually reconstructed text. These layers were part of the first version of the corpus (PDTSC 1.0). Version 2.0 is extended by an automatic dependency parser at the analytical and by the manual annotation of “deep” syntax at the tectogrammatical layer, which contains semantic roles and relations as well as annotation of coreference.
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.
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.
SiR 1.0 is a corpus of Czech articles published on iRozhlas, a news server of a Czech public radio (https://www.irozhlas.cz/). It is a collection of 1 718 articles (42 890 sentences, 614 995 words) with manually annotated attribution of citation phrases and sources. The sources are classified into several classes of named and unnamed sources.
The corpus consists of three parts, depending on the quality of the annotations:
(i) triple-annotated articles: 46 articles (933 sentences, 13 242 words) annotated independently by three annotators and subsequently curated by an arbiter,
(ii) double-annotated articles: 543 articles (12 347 sentences, 180 622 words) annotated independently by two annotators and automatically unified,
and (iii) single-annotated articles: 1 129 articles (29 610 sentences, 421 131 words) annotated each only by a single annotator.
The data were annotated in the Brat tool (https://brat.nlplab.org/) and are distributed in the Brat native format, i.e. each article is represented by the original plain text and a stand-off annotation file.
Please cite the following paper when using the corpus for your research: Hladká Barbora, Jiří Mírovský, Matyáš Kopp, Václav Moravec. Annotating Attribution in Czech News Server Articles. In: Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 1817–1823, Marseille, France 20-25 June 2022.