Information Retrieval
Information retrieval (IR) focuses on efficiently finding relevant documents or information within large datasets in response to user queries. Current research emphasizes improving retrieval accuracy and efficiency through advancements in semantic understanding, particularly using multimodal data (text, images, tables) and advanced embedding models within retrieval-augmented generation (RAG) frameworks. These improvements are crucial for various applications, including search engines, question answering systems, and knowledge-based applications across diverse domains like healthcare and legal research, ultimately enhancing access to and understanding of information.
Papers
Typo-Robust Representation Learning for Dense Retrieval
Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
Reorganizing Educational Institutional Domain using Faceted Ontological Principles
Subhashis Das, Debashis Naskar, Sayon Roy
SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs
Yamei Tu, Rui Qiu, Han-Wei Shen
Automatic retrieval of corresponding US views in longitudinal examinations
Hamideh Kerdegari, Tran Huy Nhat Phung1, Van Hao Nguyen, Thi Phuong Thao Truong, Ngoc Minh Thu Le, Thanh Phuong Le, Thi Mai Thao Le, Luigi Pisani, Linda Denehy, Vital Consortium, Reza Razavi, Louise Thwaites, Sophie Yacoub, Andrew P. King, Alberto Gomez