This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.

This study introduces an unsupervised term extraction approach that combines non-negative matrix factorization (NMF) with word embeddings. Inspired by a pioneering semantic NMF method that employs regularization to jointly optimize document–word and word–word matrix factorizations for document clustering, we adapt this strategy for term extraction. Typically, a word–word matrix representing semantic relationships between words is constructed using cosine similarities between word embeddings. However, it has been established that transformer encoder embeddings tend to reside within a narrow cone, leading to consistently high cosine similarities between words. To address this issue, we replace the conventional word–word matrix with a word–seed submatrix, restricting columns to ‘domain seeds’—specific words that encapsulate the essential semantic features of the domain. Therefore, we propose a modified NMF framework that jointly factorizes the document–word and word–seed matrices, producing more precise encoding vectors for words, which we utilize to extract high-relevancy topic-related terms. Our modification significantly improves term extraction effectiveness, marking the first implementation of semantically enhanced NMF, designed specifically for the task of term extraction. Comparative experiments demonstrate that our method outperforms both traditional NMF and advanced transformer-based methods such as KeyBERT and BERTopic. To support further research and application, we compile and manually annotate two new datasets, each containing 1000 sentences, from the ‘Geography and History’ and ‘National Heroes’ domains. These datasets are useful for both term extraction and document classification tasks. All related code and datasets are freely available.

Most business Web documents provide key information for decision-making in the form of text tables. High-speed retrieval and analysis of such tables is of great interest to the business and at the same time is a major challenge for researchers. The problem lies in the fact that text tables in Web documents are rarely self-described, i.e. contain no data schemas. In this paper we consider the special case of the problem, limited by the scope of e-commerce. The main objects of e-commerce are goods, works, services and other objects represented in text tables as sets of characteristics (attributes). In fact, these text tables compactly map domain objects into sets of “attribute-value” pairs. Thus, the extraction of meaningful information from the tables can be interpreted as extraction of sets of “attribute-value” pairs. We need to interpret these attributes correctly using a domain knowledge base, since the search engine does not have any information about neither the nature of these attributes, nor their internal relations. In this paper we use as knowledge base semi-extensible e-commerce ontology, based on economic classifiers of the Kazakhstan unified system of classification and coding of technical, economic and social information.