Abstract: Multi-label text classification involves assigning multiple relevant categories to a single text, enabling applications in academic indexing, medical diagnostics, and e-commerce. However, ...
Service-based organizations may handle thousands of customer emails daily, placing a significant burden on IT help desks, customer service organizations, and other departments involved in reading, ...
In recent decades, medical short texts, such as medical conversations and online medical inquiries, have garnered significant attention and research. The advances in the medical short text have ...
Google on Friday added a new, experimental “embedding” model for text, Gemini Embedding, to its Gemini developer API. Embedding models translate text inputs like words and phrases into numerical ...
Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent ...
In case you use any of the components for your research, please refer to (and cite) this paper: "LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in ...
In recent years, the field of text-to-speech (TTS) synthesis has seen rapid advancements, yet it remains fraught with challenges. Traditional TTS models often rely on complex architectures, including ...
Abstract: Text classification tasks aim to comprehend and classify text content into specific classifications. This task is crucial for interpreting unstructured text, making it a foundational task in ...
PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback