Information support for decision making in problem situation description

Objectives. The problem of automating the user information support in decision making system at the stage of describing the problem situation is solved. The relevance of the problem is associated with the need to collect and process significant amounts of information, since in the presence of a large number of factors, a person's capabilities are often insufficient to search and organize the necessary information.When solving the problem of user information support at the stage of describing the problem situation, three main goals are pursued: building a mathematical model of the corresponding processes; formalization of the set of basic concepts of the model; development of the algorithms for implementation of user interaction with the information system.Methods. Methods of set theory, probability theory and graph theory are used.Results. A mathematical model of user information support at the stage of describing a problem situation has been developed. In the process of interacting with the user, the system suggests special templates of sentences and texts for filling. Along with templates, the user receives help texts from the system. They are generated on the basis of the previously developed model of knowledge representation in the form of verbal associations, that is, semantic links between words and phrases corresponding to associative relationships between the entities they designate in the real world.Conclusion. As an implementation of the proposed model, the following algorithms have been developed: an algorithm for creating a dictionary of communicative fragments; algorithms for creating fragment-slot templates for sentences, texts and subject areas; an algorithm of user information support. The vocabulary of communicative fragments is created in four steps in accordance with their formal definition. At each step, four conditions from the given definition are tested sequentially. Fragment-slot templates of sentences are formed by replacing their basic communicative fragments with slots, and text templates - as tuples of templates of their sentences. Fragment-slot templates of subject areas are created in the form of implementation of binary relations reductions on the sets of sentence templates from the corresponding thematic text corpora. Each thematic body of texts defines a certain subject area.

A benchmark of Spanish language datasets for computationally driven research

In the domain of Galleries, Libraries, Archives and Museums (GLAM) institutions, creative and innovative tools and methodologies for content delivery and user engagement have recently gained international attention. New methods have been proposed to publish digital collections as datasets amenable to computational use. Standardised benchmarks can be useful to broaden the scope of machine-actionable collections and to promote cultural and linguistic diversity. In this article, we propose a methodology to select datasets for computationally driven research applied to Spanish text corpora. This work seeks to encourage Spanish and Latin American institutions to publish machine-actionable collections based on best practices and avoiding common mistakes.

Die Größe ‚Gewicht‘ im deutsch-italienischen Sprachvergleich

The topic of this article is ‘weight’ in the German-Italian language comparison. Which linguistic means are used to refer to weight in German (Gewicht) and what are the Italian equivalents? The material which has been collected is based on monolingual German and Italian dictionaries, reference works and text corpora as well as on bilingual German-Italian dictionaries and text excerpts. The classification of the so-called weight designations including derivatives, composites and word combinations is carried out from an etymological and lexical perspective. In addition to the dictionary entries, German-Italian translation examples show further equivalents of terms and expressions with regards to ‘weight’ in this language pair.

Application of corpus technologies in teaching specialized translation

Corpus technologies (corpora of English and Ukrainian texts and tools for their processing) represent modern specialized discourse and facilitate searching for and comparing different units of translation, which makes them a useful tool for both practicing and trainee translators. The purpose of this article is to determine the role and place of corpus technologies in teaching specialized translation on the example of the oil and gas industry. Comparative and parallel text corpora are characterized. The paper reveals methods of applying mono- and bilingual comparative and parallel corpora and corpus managers for acquiring knowledge about genre-stylistic features of texts; developing skills to distinguish a term and determine its collocation profile and semantic preference; analyze translation techniques; translate collocations, complex noun constructions, verbal phrases, and abbreviations. Examples of relevant exercises and tasks that should be performed at the translation training stage are given. Further research should be aimed at integrating corpus-based tasks into the translation practice stage involving the implementation of a translation project.

Analysis of a Brazilian Indigenous corpus using machine learning methods

In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible. Meanwhile there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects. Therefore, this paper proposes to study and automatically classify 26 neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models to the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.

Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora

The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer-based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains—biology, chemistry, and medicine—available in different languages—English and French. Individual deep masked language models, pretrained on external corpora, are fined-tuned on task-specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant improvement of the ensemble models over an individual BERT-based baseline model, with an overall best performance of 77% macro F1-score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.

Tracing Long-term Value Change in (Energy) Technologies: Opportunities of Probabilistic Topic Models Using Large Data Sets

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace (new) values that are (still) latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents.

Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.