Embedding the Tokenized Text

As we continue learning about the development of chatbots, it’s essential to understand how these systems generate human-like responses. This lesson introduces a very important concept in NLP, which is embedding. Embedding is an essential part of natural language processing (NLP) because it enables chatbots to capture the semantic relationships between words in high-dimensional space. By converting text into numerical vectors, embeddings allow chatbots to process and respond to user queries while understanding the context and the language of the query. Embedding forms a core element in the transformer's architecture.

In the beginning, we tokenized our text by separating sentences into tokens or words. Once the words are separated, it is time to convert these tokens into numbers or, more accurately, vector embedding. Embedding solves the problem of not being able to define the semantic relationships between words because embedding allows us to calculate the distance between the various words. It is calculated during model training whereby the algorithm analyzes the occurrence of words in the paragraph, or in other words, the concurrence patterns of words. Once the patterns are identified and isolated, the algorithm represents the words as vectors. These vectors are, in fact, coordinates in a multi-dimensional space, which allows us to calculate the distance to words. Therefore, to calculate the semantic relationship between words, the algorithm groups words with respect to their ...