Word vectors are a popular technique in natural language processing (NLP) that represent words as dense vectors in a multi-dimensional space. These vectors capture the semantic and syntactic relationships between words, making them useful for various NLP tasks such as text classification, sentiment analysis, and machine translation.
Basics of Word Vectors
Word vectors are typically generated using one of the following methods:
- Word2Vec: This method uses neural networks to learn word vectors from a large corpus of text. It captures the semantic similarity between words based on their co-occurrence in the text.
- GloVe: Global Vectors for Word Representation is another popular method that uses matrix factorization to learn word vectors. It focuses on capturing the global word-word relationships in the corpus.
Applications of Word Vectors
Word vectors have a wide range of applications in NLP:
- Text Classification: Word vectors can be used to represent the semantic content of a document, which can then be used for text classification tasks.
- Sentiment Analysis: Word vectors can help in understanding the sentiment of a text by capturing the sentiment associated with each word.
- Machine Translation: Word vectors can be used to translate words from one language to another by finding the closest vector representation in the target language.
Further Reading
For more information on word vectors, you can refer to the following resources:
Word Vector Representation