What is “word embedding” in natural language processing (NLP)?
a) A technique to represent words as continuous vectors in a high-dimensional space
b) A method to cluster similar words together
c) A way to convert text into binary format
d) A method to translate text from one language to another
Answer:
a) A technique to represent words as continuous vectors in a high-dimensional space
Explanation:
Word embedding is a technique used in natural language processing (NLP) to represent words as continuous vectors in a high-dimensional space. These vectors capture the semantic meaning of words, allowing similar words to have similar vector representations.
Popular word embedding techniques include Word2Vec, GloVe, and FastText. These methods help models understand relationships between words and improve performance in tasks such as text classification, sentiment analysis, and machine translation.
Word embeddings provide a more efficient and meaningful representation of text data compared to traditional methods like one-hot encoding, as they capture context and similarity between words.