Definition: Word embedding is a technique used to represent words as numerical vectors in a high-dimensional space.

Detailed Description:

Word embedding captures the semantic relationships between words, allowing computers to understand the meaning and context of text. Common word embedding techniques include:

• Word2Vec: A neural network-based technique that learns word embeddings from a large corpus of text.
• GloVe: A statistical model that learns word embeddings based on co-occurrence statistics.
• FastText: A technique that considers subword information to improve word embeddings.

Word embeddings are used in various natural language processing tasks, such as:

• Text classification
• Machine translation
• Question answering
• Sentiment analysis

Related Terms:

• Natural Language Processing (NLP)
• Machine Learning
• Vector Space Model
• Distributed Representation

FAQs:

1. How are word embeddings used in natural language processing?

   • Word embeddings can be used to capture semantic relationships between words, which can improve the performance of NLP tasks.

2. What are the advantages of using word embeddings?

   • Word embeddings can capture semantic relationships between words, which can improve the accuracy of NLP tasks.

3. What are some challenges in learning word embeddings?

   • Challenges include dealing with rare words, polysemy, and the curse of dimensionality.