Research & Academic Work
Exploring the frontiers of Natural Language Processing through cutting-edge research and innovative applications. My work bridges theoretical foundations with practical solutions to advance AI capabilities.
Research Interests
Natural Language Processing
Cross-Domain Information Extraction
Transformer Models & Large Language Models
Named Entity Recognition
Heterogeneous Document Processing
Text Classification & Sentiment Analysis
Deep Learning for NLP
AI Applications in Real-World Scenarios
Current Research
A New Deep Learning Approach for Cross-Domain Information Extraction in Heterogeneous Textual Documents
My PhD research focuses on developing novel deep learning architectures for cross-domain information extraction from heterogeneous textual documents. The research addresses the challenge of extracting structured information from diverse document types and domains using advanced neural network models, with particular emphasis on transfer learning and domain adaptation techniques.
Research Objectives
- •Design and develop deep learning models for cross-domain information extraction
- •Address challenges in processing heterogeneous textual documents
- •Improve generalization capabilities across different domains and document types
- •Create robust extraction systems that adapt to various text formats and structures
Methodologies
- •Deep Learning Architectures (Transformers, CNNs, RNNs)
- •Cross-Domain Transfer Learning
- •Domain Adaptation Techniques
- •Hybrid Neural Network Models
- •Information Extraction Pipelines
Academic Projects
Hybrid BERT + BiLSTM + Attention Architecture for Financial NER
Developed a sophisticated hybrid architecture combining BERT embeddings with BiLSTM and attention mechanisms for Named Entity Recognition in financial texts. Implemented multi-branch fusion mechanisms to enhance entity extraction accuracy in complex financial documents.