This paper discusses two approaches to the diachronic normalization of Polish texts: a rule-based solution that relies on a set of handcrafted patterns, and a neural normalization model based on the text-to-text transfer transformer architecture. The training and evaluation data prepared for the task are discussed in detail, along with experiments conducted to compare the proposed normalization solutions. A quantitative and qualitative analysis is made. It is shown that at the current stage of inquiry into the problem, the rule-based solution outperforms the neural one on 3 out of 4 variants of the prepared dataset, although in practice both approaches have distinct advantages and disadvantages.

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.

In this article, we discuss the conditions surrounding the building of historical and literary corpora. We describe the assumptions and method of making the original corpus of the Polish novel (1864-1939). Then, we present the research procedure aimed at demonstrating the variability of the emotional value of the concept of “the city” and “the country” in the texts included in our corpus. The proposed method considers the complex socio-political nature of Central and Eastern Europe, especially the fact that there was no unified Polish state during this period. The method can be easily replicated in studies of the literature of countries with similar specificities.

The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.

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.

This paper presents the PolNet-Polish WordNet project which aims at building a linguistically oriented ontology for Polish compatible with other WordNet projects such as Princeton WordNet, EuroWordNet and other similarly organized ontologies. The main idea behind this kind of ontologies is to use words related by synonymy to construct formal representations of concepts. In the paper we sketch the PolNet project methodology and implementation. We present data obtained so far, as well as the WQuery tool for querying and maintaining PolNet. WQuery is a query language that make use of data types based on synsets, word senses and various semantic relations which occur in wordnet-like lexical databases. The tool is particularly useful to deal with complex querying tasks like searching for cycles in semantic relations, finding isolated synsets or computing overall statistics. Both data and tools presented in this paper have been applied within an advanced AI system POLINT-112-SMS with emulated natural language competence, where they are used in the understanding subsystem.