Analysis of Images, Social Networks and Texts: 12th International Conference, AIST 2024, Bishkek, Kyrgyzstan, October 17-19, 2024, Revised Selected Papers

by Alexander Panchenko (Editor), Dmitry Gubanov (Editor), Michael Khachay (Editor), Andrey Kutuzov (Editor), Natalia Loukachevitch (Editor)

Overview

This book constitutes the refereed proceedings of the 12th International Conference on Analysis of Images, Social Networks and Texts, AIST 2024, held in Bishkek, Kyrgyzstan, during October 17–19, 2024.

The 12 papers included in this book were carefully reviewed and selected from 54 submissions. They were organized in topical sections as follows: natural language processing; computer vision; and data analysis and machine learning.

Product Details

ISBN-13:	9783031970184
Publisher:	Springer Nature Switzerland
Publication date:	08/02/2025
Series:	Communications in Computer and Information Science , #2364
Pages:	158
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Natural Language Processing.- Leveraging Large Language Models in Code Question Answering: Baselines and Issues.- Structured Data from Dictionary Text: Applying LLMs for Low-Resource Cross-Lingual Information Extraction.- Annotation of Greeting and Introduction in Russian Spoken Dialogues Using ISO 24617-2:2020.- Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies.- Computational Linguistics and Pedagogy: Exploring Lexical Patterns of School Teachers.- Research on Content Dissemination in Social Networks Taking into Account Users’ Influence Potential.- MoRAE: Movie Review Dataset for Aspect Extraction.- Diagnosis of the Severity of Depression Using Speech Recording Analysis.- RuCLEVR: A Russian Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning.- Computer Vision.- Efficient Detection of Small-Scale Microorganisms in Microscopic Photography.- Deep Learning Approach for Offline Nepali Handwritten Word Recognition.- Data Analysis and Machine Learning.- Evaluating Deep Learning-Based Speaker Verification Systems: A Comparative Study Across Open-Source and Forensic Datasets.