Search Results for author:
Found 8 papers, 4 papers with code
CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor assignment in Spanish
In this paper, we describe our participation in the MESINESP Task of the BioASQ biomedical semantic indexing challenge.
Design and Implementation of a Tool for Extracting Uzbek Syllables
In this paper, we present a comprehensive approach to syllabification for the Uzbek language, including rule-based techniques and machine learning algorithms.
Text classification dataset and analysis for Uzbek language
We also present a comprehensive evaluation of different models, ranging from traditional bag-of-words models to deep learning architectures, on this newly created dataset.
UzbekTagger: The rule-based POS tagger for Uzbek language
This research paper presents a part-of-speech (POS) annotated dataset and tagger tool for the low-resource Uzbek language.
Uzbek Sentiment Analysis based on local Restaurant Reviews
Extracting useful information for sentiment analysis and classification problems from a big amount of user-generated feedback, such as restaurant reviews, is a crucial task of natural language processing, which is not only for customer satisfaction where it can give personalized services, but can also influence the further development of a company.
A machine transliteration tool between Uzbek alphabets
Machine transliteration, as defined in this paper, is a process of automatically transforming written script of words from a source alphabet into words of another target alphabet within the same language, while preserving their meaning, as well as pronunciation.
SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language
Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task.
Cross-Lingual Word Embeddings for Turkic Languages
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.
