Abstract People express their opinions and views in different and often ambiguous ways, hence the meaning of their words is often not explicitly stated and frequently depends on the context. Therefore, it is difficult for machines to process and understand the information conveyed in human languages. This work addresses the problem of sentiment analysis (SA). We propose a simple yet comprehensive method which uses contextual embeddings and a self-attention mechanism to detect and classify sentiment. We perform experiments on reviews from different domains, as well as on languages from three different language families, including morphologically rich Polish and German. We show that our approach is on a par with state-of-the-art models or even outperforms them in several cases. Our work also demonstrates the superiority of models leveraging contextual embeddings. In sum, in this paper we make a step towards building a universal, multilingual sentiment classifier.

The paper is devoted to the issue of clustering small sets of very short texts. Such texts are often incomplete and highly inconclusive, so establishing a notion of proximity between them is a challenging task. In order to cope with polysemy we adapt the SenseSearcher algorithm (SnS), by Kozlowski and Rybinski in Computational Intelligence 33(3): 335–367, 2017b . In addition, we test the possibilities of improving the quality of clustering ultra-short texts by means of enriching them semantically. We present two approaches, one based on neural-based distributional models, and the other based on external knowledge resources. The approaches are tested on SnSRC and other knowledge-poor algorithms.

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than document-centric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.

The paper addresses the problem of automatic dictionary translation.The proposed method translates a dictionary by means of mining repositories in the source and target languages, without any directly given relationships connecting the two languages. It consists of two stages: (1) translation by lexical similarity, where words are compared graphically, and (2) translation by semantic similarity, where contexts are compared. In the experiments Polish and English version of Wikipedia were used as text corpora. The method and its phases are thoroughly analyzed. The results allow implementing this method in human-in-the-middle systems.

The use of first-order logic as database logic is shown to be powerful enough for formalizing and implementing not only relational but also hierarchical and network-type databases. It enables one to treat all the types of databases in a uniform manner. This paper focuses on the database language for heterogeneous databases. The language is shown to be general enough to specify constraints for a particular type of database, so that a specification of database type can be “translated” to the specification given in the database language, creating a “logical environment” for different views that can be defined by users. Owing to the fact that any database schema is seen as a first-order theory expressed by a finite set of sentences, the problems concerned with completeness and compactness of the database logic discussed by Jacobs ("On Database Logic,” J. ACM 29 ,2 (Apr. 1982), 310-332) are avoided.