This paper presents the POLygraph dataset, a unique resource for fake news detection in Polish. The dataset, created by an interdisciplinary team, is composed of two parts: the “fake-or-not” dataset with 11,360 pairs of news articles (identified by their URLs) and corresponding labels, and the “fake-they-say” dataset with 5,082 news articles (identified by their URLs) and tweets commenting on them. Unlike existing datasets, POLygraph encompasses a variety of approaches from source literature, providing a comprehensive resource for fake news detection. The data was collected through manual annotation by expert and non-expert annotators. The project also developed a software tool that uses advanced machine learning techniques to analyze the data and determine content authenticity. The tool and dataset are expected to benefit various entities, from public sector institutions to publishers and fact-checking organizations. Further dataset exploration will foster fake news detection and potentially stimulate the implementation of similar models in other languages. The paper focuses on the creation and composition of the dataset, so it does not include a detailed evaluation of the software tool for content authenticity analysis, which is planned at a later stage of the project.

In a spoken dialogue system, an NLU model is preceded by a speech recognition system that can deteriorate the performance of natural language understanding. This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models. The proposed method combines the back transcription procedure with a fine-grained technique for categorizing the errors that affect the performance of NLU models. The method relies on the usage of synthesized speech for NLU evaluation. We show that the use of synthesized speech in place of audio recording does not change the outcomes of the presented technique in a significant way.

This paper presents a toolkit that applies named-entity extraction techniques to identify information related to criminal activity in texts from the Polish Internet. The methodological and technical assumptions were established following the requirements of our application users from the Border Guard. Due to the specificity of the users’ needs and the specificity of web texts, we used original methodologies related to the search for desired texts, the creation of domain lexicons, the annotation of the collected text resources, and the combination of rule-based and machine-learning techniques for extracting the information desired by the user. The performance of our tools has been evaluated on 6240 manually annotated text fragments collected from Internet sources. Evaluation results and user feedback show that our approach is feasible and has potential value for real-life applications in the daily work of border guards. Lexical lookup combined with hand-crafted rules and regular expressions, supported by text statistics, can make a decent specialized entity recognition system in the absence of large data sets required for training a good neural network.

The paper describes a system for end-user development using natural language. Our approach uses a ranking model to identify the actions to be executed followed by reference and parameter matching models to select parameter values that should be set for the given commands. We discuss the results of evaluation and possible improvements for future work.