What does a word embedding represent in the context of natural language processing?

A word embedding is a technique used in natural language processing to represent words as high-dimensional vectors that capture the semantic meaning and relationships between them. The idea behind word embeddings is that words that have similar meanings or are used in similar contexts will be mapped to nearby points in this high-dimensional space. This allows the model to better understand and process natural language by recognizing patterns and similarities between words, which is crucial for various NLP tasks such as sentiment analysis, translation, and information retrieval.

In contrast, the other options do not accurately capture the essence of word embeddings. Fixed-length numerical vectors of phrases refer more to concepts like fixed vector representations rather than the dynamic nature of embeddings which can apply varying dimensions based on context. Simple frequency counts are a method of representing text based on how often words appear, which lacks the nuanced understanding that embeddings provide. Basic character embeddings focus on the structure of words rather than their meanings, making them less effective for understanding the semantic relationships that word embeddings are designed to represent. Such distinctions clarify why high-dimensional representations of words are fundamental in capturing the richness of language.