Word2Vec
Word2Vec is a pioneering neural network model that learns word embeddings from large text corpora. Developed by Google in 2013, it demonstrated that vector arithmetic on word embeddings captures semantic relationships.
Understanding Word2Vec
Word2Vec is a pioneering neural network-based method developed by researchers at Google in 2013 that learns word embeddings by predicting words from their context (Skip-gram) or context from words (Continuous Bag of Words). It demonstrated that simple shallow neural networks trained on large text corpora could produce remarkably rich vector representations capturing both syntactic and semantic relationships. The famous analogy "king - man + woman = queen" showcased Word2Vec's ability to encode relational knowledge geometrically. While newer contextual embedding methods from transformer models like BERT have superseded Word2Vec for many tasks, its fundamental insight that distributional semantics can be captured through neural prediction tasks laid the groundwork for modern natural language processing. Word2Vec remains influential in understanding how meaning can be represented computationally and is still used in resource-constrained applications.
Category
Natural Language Processing
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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.