Advancements in AI and natural language processing have revolutionized machine-human language interactions, with question answering (QA) systems playing a pivotal role. The knowledge base question answering (KBQA) task, utilizing structured knowledge graphs (KG), allows for handling extensive knowledge-intensive questions. However, a significant gap exists in KBQA datasets, especially for low-resource languages. Many existing construction pipelines for these datasets are outdated and inefficient in human labor, and modern assisting tools like Large Language Models (LLM) are not utilized to reduce the workload. To address this, we have designed and implemented a modern, semi-automated approach for creating datasets, encompassing tasks such as KBQA, Machine Reading Comprehension (MRC), and Information Retrieval (IR), tailored explicitly for low-resource environments. We executed this pipeline and introduced the PUGG dataset, the first Polish KBQA dataset, and novel datasets for MRC and IR. Additionally, we provide a comprehensive implementation, insightful findings, detailed statistics, and evaluation of baseline models.

The article discusses the challenges of cross-linguistic dialogue act annotation, which involves using methods developed for one language to annotate conversations in another language. The article specifically focuses on the research on dialogue act annotation in Polish, based on the ISO standard developed for English. The article examines the differences between Polish and English in dialogue act annotation based on selected examples from DiaBiz.Kom corpus, such as the use of honorifics in Polish, the use of inflection to convey meaning in Polish, the tendency to use complex sentence structures in Polish, and the cultural differences that may play a role in the annotation of dialogue acts. The article also discusses the creation of DiaBiz.Kom, a Polish dialogue corpus based on ISO 24617-2 standard applied to 1100 transcripts.

This article presents the specification and evaluation of DiaBiz.Kom – the corpus of dialogue texts in Polish. The corpus contains transcriptions of telephone conversations conducted according to a prepared scenario. The transcripts of conversations have been manually annotated with a layer of information concerning communicative functions. DiaBiz.Kom is the first corpus of this type prepared for the Polish language and will be used to develop a system of dialog analysis and modules for creating advanced chatbots.

In the paper, we focus on modeling spatial expressions in texts. We present the guidelines used to annotate the PST 2.0 (Corpus of Polish Spatial Texts) — a corpus designed for training and testing the tools for spatial expression recognition. The corpus contains a set of texts gathered from texts collected from travel blogs available under Creative Commons license. We have defined our guidelines based on three existing specifications for English (SpatialML, SpatialRole Labelling from SemEval-2013 Task 3 and ISO-Space1.4 from SpaceEval 2014). We briefly present the existing specifications and discuss what modifications have been made to adapt the guidelines to the characteristics of the Polish language. We also describe the process of data collection and manual annotation, including inter-annotator agreement calculation and corpus statistics. In the end, we present detailed statistics of the PST 2.0 corpus, which include the number of components, relations, expressions, and the most common values of spatial indicators, motion indicators, path indicators, distances, directions, and regions.

In the paper we present the latest changes introduce to Inforex — a web-based system for qualitative and collaborative text corpora annotation and analysis. One of the most important news is the release of source codes. Now the system is available on the GitHub repository (https://github.com/CLARIN-PL/Inforex) as an open source project. The system can be easily setup and run in a Docker container what simplifies the installation process. The major improvements include: semi-automatic text annotation, multilingual text preprocessing using CLARIN-PL web services, morphological tagging of XML documents, improved editor for annotation attribute, batch annotation attribute editor, morphological disambiguation, extended word sense annotation. This paper contains a brief description of the mentioned improvements. We also present two use cases in which various Inforex features were used and tested in real-life projects.

In this paper we present a morpho-syntactic tagger dedicated to Computer-mediated Communication texts in Polish. Its construction is based on an expanded RNN-based neural network adapted to the work on noisy texts. Among several techniques, the tagger utilises fastText embedding vectors, sequential character embedding vectors, and Brown clustering for the coarse-grained representation of sentence structures. In addition a set of manually written rules was proposed for post-processing. The system was trained to disambiguate descriptions of words in relation to Parts of Speech tags together with the full morphological information in terms of values for the different grammatical categories. We present also evaluation of several model variants on the gold standard annotated CMC data, comparison to the state-of-the-art taggers for Polish and error analysis. The proposed tagger shows significantly better results in this domain and demonstrates the viability of adaptation.

We report a first major upgrade of Inforex — a web-based system for qualitative and collaborative text corpora annotation and analysis. Inforex is a part of Polish CLARIN infrastructure. It is integrated with a digital repository for storing and publishing language resources and allows to visualize, browse and annotate text corpora stored in the repository. As a result of a series of workshops for researches from humanities and social sciences fields we improved the graphical interface to make the system more friendly and readable for non-experienced users. We also implemented a new functionality for gold standard annotation which includes private annotations and annotation agreement by a super-annotator.

In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and lemmatize named entities for Polish.