Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence Graphs

Janu, Neha; Singh, Anjali; Nawal, Meenakshi; Gupta, Sunita; Kumar, Tapesh; Singh, Vijendra Pal

doi:10.1201/9781003272649-9

Cited by 6 publications

(1 citation statement)

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“…Some contributions focus on semantic parsing via multilingual translation (Procopio et al, 2021), generic frameworks for multilingual short text categorization (Enamoto et al, 2021), and semantic approaches for document classification using deep neural networks and multimedia knowledge graphs (Rinaldi et al, 2021). Additionally, there are studies addressing cross-lingual word sense disambiguation (Janu et al, 2022), semantic graph-based topic modeling for multilingual fake news detection (Mohawesh et al, 2023), and automatic Bangla knowledge graph construction with semantic neural graph filtering (Wasi et al, 2024). Moreover, the exploration extends to investigating the feasibility of machine translation as an alternative for multilingual question-answering systems over knowledge graphs (Perevalov et al, 2022) and comparing information retrieval versus deep learning approaches for generating traceability links in bilingual projects (Lin et al, 2022).Spanning various domains and methodologies, these studies collectively contribute to advancing the field's understanding and capabilities.…”

Section: Related Workmentioning

confidence: 99%

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Hao Yang

2024

jes

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Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilitating the development of more robust translation systems that are capable of handling diverse language pairs and domains. The paper introduces a novel approach termed the Multilingual Ant Bee Optimization Graph Neural Network (MABO-GNN) for addressing optimization, classification, and multilingual translation tasks. MABO-GNN integrates ant bee optimization algorithms with graph neural networks to provide a versatile framework capable of optimizing objective functions, improving classification accuracy iteratively, and facilitating high-quality translations across multiple languages. Through comprehensive experimentation, the efficacy of MABO-GNN is demonstrated across various tasks, languages, and datasets. in optimization experiments, MABO-GNN achieves objective function values of 0.012, 0.015, 0.011, and 0.013 in Experiment 1, Experiment 2, Experiment 3, and Experiment 4, respectively, with convergence times ranging from 90 to 150 seconds. In classification tasks, the model exhibits notable performance improvements over iterations, with BLEU scores reaching 0.84 and METEOR scores reaching 0.78 in the fifth iteration. The translation results showcase BLEU scores of 0.85 for English, 0.82 for French, 0.79 for German, 0.81 for Spanish, and 0.75 for Chinese, indicating the model's proficiency in generating high-quality translations across diverse languages.

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Section: Related Workmentioning

confidence: 99%

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Hao Yang

2024

jes

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COVID-19 epidemic anxiety, mental stress, and sleep disorders in developing country university students

Pandey

Hasan

Pandey

et al. 2023

CSIT

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COVID-19 damaged the quality of sleep and mental stamina worldwide despite public health initiatives. Problems with sleep can damage health and academic performance, thus university students should know their frequency and causes. This study explored university students’ COVID-19 Anxiety, Mental Stress, and Sleep Disorders. The internet-deployed transversal analysis includes 443 Indian and Ethiopian students from April 1 to 13, 2021. After creating a Google form link, respondents received the survey via WhatsApp, E-mail, Telegram, and others. Pittsburgh Sleep Quality Index examined student sleep concerns. Descriptive and inferential studies estimated sleep disruption frequency and causes. Logistic regression and chi-squared testing found sleep problems in Indian and Ethiopian university students. The researcher examined SPSS 25 data. 54.7% had sleep difficulties. Being female [Conditional Odds Ratio = 4.32, 95 percent Confidence interval (Lower-1.807)–(Upper-10.370)], smoking [2.81, 95 percent CI (Lower-1.609)–(Upper-4.920)], spending 14 days in quarantine [1.87, 95 percent CI (Lower-1.042)–(Upper-3.373)], and having a family member with COVID-19 [1.94, 95 percent CI (Lower-1.anxiety [Conditional Odds Ratio = 4.01, 95 percent CI (Lo Ethiopian and Indian pupils sleep poorly. Female gender, smoking, 14-day quarantine, and sleep troubles were connected to COVID-19 dread, COVID-19-infected family member, viral exposure, depression, anxiety, and stress in Indian and Ethiopian university students. Interventions should increase university students’ sleep.

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Globalization and Emerging Opportunities and Challenges in Sustainable Environment in Industry 4.0

Swapna

Singh

Madaan

et al. 2023

Handbook of Research on Safe Disposal Methods of Municipal Solid Wastes for a Sustainable Environment

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The rapid technological advancement of the manufacturing sector over the past few decades inevitably led to the rise of Industry 4.0. It has the potential to significantly alter how globalisation is practiced in the production and consumption of products and services across international markets. In this chapter, the authors take a closer look at the rise of Industry 4.0 and the new technical architecture that underpins it, as well as the benefits it's expected to bring. In addition, how technology can be used to fortify a company's competitiveness and shield it from the perils of this transformation. The new global division of labour, the worldwide supply chain, and the global value chain will all be profoundly affected by this multifaceted technology, as evidenced by a thorough examination of the relevant literature. It will alter the competitive landscape by shifting the advantage away from large corporations and toward small and medium-sized enterprises (SMEs) in emerging marketsand developed markets. As human and technological skills advance quickly, businesses may be able to profit.

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Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence Graphs

Cited by 6 publications

References 0 publications

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

COVID-19 epidemic anxiety, mental stress, and sleep disorders in developing country university students

Globalization and Emerging Opportunities and Challenges in Sustainable Environment in Industry 4.0

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