Encoding is the process of converting text data into a different format, such as a binary representation or a numerical representation. This is often done in order to make the data more compact or easier to process by a computer. For example, one-hot encoding is a common encoding technique that converts each word in a sentence into a vector of zeros, with a single one in the position corresponding to the word.
Embedding is the process of converting text data into a vector representation that captures the meaning of the words and their relationships to each other. This is often done in order to make the data more useful for natural language processing (NLP) tasks, such as machine translation and text summarization. For example, word2vec is a common embedding technique that learns vector representations of words based on their co-occurrence patterns in a large corpus of text.
Here is a table summarizing the key differences between embedding and encoding:
| Feature | Embedding | Encoding |
|---|---|---|
| Purpose | Capture the meaning of text | Convert text to a different format |
| Representation | Vectors that capture semantic relationships | Binary or numerical representations |
| Application | Natural language processing (NLP) tasks | Data compression, storage, and transmission |
See Also: Tokenization, Embedding, Embedding space