A simple example of embeddings. In the context of word embeddings and methods like Word2Vec, two words will be near each other in the embedding space primarily if they show up in similar contexts in sentences, not necessarily because they have similar meanings. An n-gram is a continuous sequence of 'n' items from a given sample of text or speech. It is commonly used in text processing and statistics to predict the next item in a sequence. For example, in the sentence "I love to play," the 2-grams (or bigrams) would be: "I love," "love to," and "to play." The underlying principle is the distributional hypothesis, which states that words that occur in the same contexts tend to have similar meanings. So, while the primary mechanism driving the positioning of words in the embedding space is their context, there's an indirect implication about their semantic similarity.
Tasks: Natural Language Processing, Deep Learning Fundamentals, Sentence Similarity
Task Categories: Natural Language Processing, Deep Learning Fundamentals