Word Embedding
A word embedding is a dense vector representation of a word that captures its semantic meaning and relationships to other words. Words with similar meanings are mapped to nearby points in embedding space.
Understanding Word Embedding
Word embeddings are dense vector representations that map words into a continuous numerical space where semantic relationships are preserved as geometric properties. Unlike sparse one-hot encodings, embeddings capture meaning by positioning similar words close together, so "king" and "queen" share nearby coordinates while being distant from unrelated terms like "bicycle." Foundational methods include Word2Vec and GloVe, which learn embeddings from word co-occurrence patterns in large text corpora. These representations enable mathematical operations on language, famously demonstrating that vector("king") minus vector("man") plus vector("woman") approximates vector("queen"). Modern contextual embeddings from transformer models like BERT generate different vectors for the same word depending on surrounding context, capturing polysemy and nuance. Word embeddings underpin virtually all natural language processing applications, from semantic search to text classification and machine translation.
Category
Natural Language Processing
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Natural Language Processing Terms
Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.