Display EMNLP 2022 papers on an interactive map
Chat about images using text prompts
Display real-time analytics and chat insights
Explore interactive maps of textual data
Display a loading spinner and prepare space
Explore data leakage in machine learning models
Display upcoming Free Fire events
Display service status updates
Generate answers to questions about images
demo of batch processing with moondream
Generate dynamic torus knots with random colors and lighting
Display spinning logo while loading
Ask questions about images of documents
EMNLP 2022 Papers is an interactive tool designed to display and explore papers from the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides a visual and intuitive way to navigate through the conference's research contributions, making it easier for researchers and practitioners to discover and analyze the latest advancements in NLP.
• Interactive Map: Visualizes the papers in a 2D space, allowing users to explore them based on semantic similarities and topics. • Paper Summaries: Provides brief summaries and key details for each paper, including titles, authors, and affiliations. • Filtering Options: Enables users to filter papers by specific topics, authors, or keywords for focused exploration. • Visualization of Trends: Highlights emerging trends and hot topics in NLP based on the distribution of papers on the map. • Search Functionality: Includes a search bar to quickly find specific papers or authors. • Export Capabilities: Allows users to export data for further analysis or reference.
What browsers are supported by EMNLP 2022 Papers?
EMNLP 2022 Papers is optimized for modern browsers like Chrome, Firefox, and Safari. Ensure you are using the latest version for the best experience.
Can I download the full papers from the tool?
No, the tool provides summaries and metadata. To access full papers, you may need to visit the official EMNLP 2022 proceedings or other academic databases.
How is the semantic similarity determined for the interactive map?
The map uses advanced NLP techniques to analyze paper abstracts and titles, embedding them into a 2D space based on semantic similarities. This allows related papers to appear closer together.