The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

This study is dedicated to one of the most difficult topics in Russian - this is the science of idioms. A feature of the Russian speech application in recent decades has been the effective change in the lexical composition of the Russian language. This article illustrates the semantic, grammatical and stylistic structural characteristics of the linguistic units that appeared in Russian at the end of the 20th century - the beginning of the twenty-first century. This work defines these terms : terminology, linguistic unity, and terms that are used as synonyms. In this research , we will adhere to the conciliatory view of the problem and include not only the terminology in the language units, but also the terminological compositions, saying

Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

Background: Schneiderian first rank symptoms are
considered highly valuable in the diagnosis of
schneideria.
They are more evident in the acute phase of the
disorder and fading gradually with time. Many studies
have shown that the rate of these symptoms are
variable in different countries and are colored by
cultural beliefs and values.
Objectives: To find out the rate of Schneiderian first
rank symptoms among newly diagnosed schizophrenic
patients, to assess which symptom(s) might
predominate in those patients, and to find out if there
is/are any correlation(s) between the occurrence of
these symptoms and the sex of the patients.
Methods: Out of twenty-four patients with no past
psychiatric hi

Translation as a human endeavor has occupied the attention of nations since it bridges the gab between cultures and helps in bringing out national integration. The translation of Kurdish literature started with personal efforts in which newspapers and magazines had played a vital role in supporting translation and paved the way for promoting the publication of Kurdish products.

      The bulk of the materials translated from Arabic exceeds that translated from other languages owing to the influence of religious and authoritarian factors.

The survey of the Kurdish journals was limited to the period 1898-1991 since it marked a radical and historic change represented by the birth of Kurdish journalis