word2vec - Distributed Representations of Words and Phrases. Tutorial.
Автор: AI Podcast Series. Byte Goose AI.
Загружено: 2025-10-20
Просмотров: 30
Distributed Representations of Words and Phrases.
The tutorial provideds the overview of the academic paper titled "Distributed Representations of Words and Phrases and their Compositionality," authored by several researchers from Google Inc. This paper introduces extensions to the continuous Skip-gram model, an efficient technique for generating high-quality distributed vector representations of words. Key enhancements discussed include subsampling frequent words to significantly increase training speed and improve vector quality for less frequent words, and Negative Sampling, a simplified alternative to the hierarchical softmax for efficient model training. Furthermore, the work addresses the limitation of word representations in handling idiomatic phrases by presenting a simple method to learn vector representations for entire phrases, demonstrating that these vectors exhibit linear, additive compositionality for performing tasks like analogical reasoning.
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