Representing words as numerical vectors is fundamental to modern natural language processing. This involves mapping words to points in a high-dimensional space, where semantically similar words are located closer together. Effective methods aim to capture relationships like synonyms (e.g., “happy” and “joyful”) and analogies (e.g., “king” is to “man” as “queen” is to “woman”) within the vector space. For example, a well-trained model might place “cat” and “dog” closer together than “cat” and “car,” reflecting their shared category of domestic animals. The quality of these representations directly impacts the performance of downstream tasks like machine translation, sentiment analysis, and information retrieval.
Accurately modeling semantic relationships has become increasingly important with the growing volume of textual data. Robust vector representations enable computers to understand and process human language with greater precision, unlocking opportunities for improved search engines, more nuanced chatbots, and more accurate text classification. Early approaches like one-hot encoding were limited in their ability to capture semantic similarities. Developments such as word2vec and GloVe marked significant advancements, introducing predictive models that learn from vast text corpora and capture richer semantic relationships.
