Verb detection is a fundamental task in natural language processing that involves identifying the action or state expressed by a verb in a sentence. However, in Uzbek language, verb detection is challenging due to the complexity of its morphology and the agglutinative nature of the language. In this paper, we propose a rule-based approach for verb detection in Uzbek texts based on affixes/suffixes. Our method is based on a set of rules that capture the morphological patterns of verb forms in Uzbek language. We evaluate the proposed approach on a dataset of Uzbek texts and report an F1-score of 0.97, which outperforms existing methods for verb detection in Uzbek language. Our results suggest that rule-based approaches can be effective for verb detection in Uzbek texts and have potential applications in various natural language processing tasks.

Recent advances in neural networks based language representation made it possible for pretrained language models to outperform previous models in many downstream natural language processing (NLP) tasks. These pretrained language models have also shown that if large enough, they exhibit good few-shot abilities, which is especially beneficial for low-resource scenarios. In this respect, although there are some large-scale multilingual pretrained language models available, language-specific pretrained models have demonstrated to be more accurate for monolingual evaluation setups. In this work, we present BERTbek - pretrained language models based on the BERT (Bidirectional Encoder Representations from Transformers) architecture for the low-resource Uzbek language. We also provide a comprehensive evaluation of the models on a number of NLP tasks: sentiment analysis, multi-label topic classification, and named entity recognition, comparing the models with various machine learning methods as well as multilingual BERT (mBERT). Experimental results indicate that our models outperform mBERT and other task-specific baseline models in all three tasks. Additionally, we also show the impact of training data size and quality on the downstream performance of BERT models, by training three different models with different text sources and corpus sizes.

The objective of enhancing the availability of natural language processing technologies for low-resource languages has significant importance in facilitating technological accessibility within the populations of speakers of these languages. Our current grasping shows that there are no established linguistic resources available open source to develop aspect-based sentiment analysis (ABSA) tools tailored to the Uzbek language. This work aims to address the aforementioned gap by presenting the first high-quality annotated ABSA dataset - UzABSA. The data used in this study was obtained from a compilation of online reviews of Uzbek restaurants. Consequently, the constructed dataset has a length of 3500 reviews at the document level and 6100+ sentences at the sentence level. The popular approach to language resources of this kind explores four distinctive characteristics, namely Aspect Terms, Aspect Term Polarities, Aspect Category Terms, as well as Aspect Category Polarities. To the best of our knowledge, it is the first and the largest ABSA dataset for the Uzbek language. To evaluate the annotation process of our dataset, we used established statistical techniques such as Cohen’s kappa coefficient and Krippendorff’s 𝛼 to assess agreement between annotators. Subsequently, a classification model, namely K-Nearest Neighbour (KNN), was used to evaluate the performance of the created dataset. Both sets of evaluation techniques demonstrate comparable levels of accuracy. The first findings across the various tasks showed promising outcomes, with accuracy rates ranging from 72% to 88%. This study not only highlights the significance of our acquired dataset but also plays a valuable tool for scholars interested in furthering sentiment analysis in the Uzbek language.

Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task. In this paper, we present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for the low-resource Uzbek language. The dataset consists of more than a thousand pairs of words carefully selected based on their morphological features, occurrence frequency, semantic relation, as well as annotated by eleven native Uzbek speakers from different age groups and gender. We also paid attention to the problem of dealing with rare words and out-of-vocabulary words to thoroughly evaluate the robustness of semantic models.

There has been an increasing interest in learning cross-lingual word embeddings to transfer knowledge obtained from a resource-rich language, such as English, to lower-resource languages for which annotated data is scarce, such as Turkish, Russian, and many others. In this paper, we present the first viability study of established techniques to align monolingual embedding spaces for Turkish, Uzbek, Azeri, Kazakh and Kyrgyz, members of the Turkic family which is heavily affected by the low-resource constraint. Those techniques are known to require little explicit supervision, mainly in the form of bilingual dictionaries, hence being easily adaptable to different domains, including low-resource ones. We obtain new bilingual dictionaries and new word embeddings for these languages and show the steps for obtaining cross-lingual word embeddings using state-of-the-art techniques. Then, we evaluate the results using the bilingual dictionary induction task. Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together. Furthermore, evaluation on an extrinsic task (Sentiment analysis on Uzbek) proves that monolingual word embeddings can, although slightly, benefit from cross-lingual alignments.