Learning Word Embedding
Word embedding is a technique used to transform free-text words into numeric values that can be processed by machine learning models. One-hot encoding is a simple approach where each distinct word corresponds to one dimension in the resulting vector, with binary values indicating presence (1) or absence (0). This transformation helps machines understand and process natural language.
Word embedding is a technique used to transform free-text words into numeric values that can be processed by machine learning models. One-hot encoding is a simple approach where each distinct word corresponds to one dimension in the resulting vector, with binary values indicating presence (1) or absence (0). This transformation helps machines understand and process natural language.
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Why it matters: Understanding how to transform free-text words into numerical representations is crucial for building effective natural language processing models. Word embedding techniques like this one are essential for engineers working on AI applications that involve text analysis, such as chatbots, sentiment analysis, or text classification.
Source: https://lilianweng.github.io/posts/2017-10-15-word-embedding/
This article was originally published at: https://lilianweng.github.io/posts/2017-10-15-word-embedding/