su-mana-s/Semantic-Communication
Semantic Message Extraction for Text Based Data With Deep Neural Nets
This project helps improve text communication efficiency by extracting only the essential meaning from text messages. It takes an original text message as input, processes it to identify and encode its core semantic content, and then outputs a reconstructed message that aims to retain the original meaning while potentially reducing the amount of data needed for transmission. This is for communication engineers or researchers working on next-generation communication systems who need to optimize data transfer for textual information.
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Use this if you are a communication systems researcher or engineer exploring methods to transmit text-based information more efficiently by focusing on semantic content rather than raw data.
Not ideal if you need to ensure perfect, bit-for-bit reconstruction of the original text or if your primary concern is with non-textual data.
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Python
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Last pushed
Sep 25, 2025
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