A data scientist is developing a natural language processing model to analyze a large corpus of legal texts. The primary objective is to generate accurate vector representations for all terms, including many specialized and infrequent legal phrases. Given this priority, which Word2vec architectural choice and training optimization method would be the most effective combination?
Continuous Bag-of-Words (CBOW) with hierarchical softmax.
Continuous Bag-of-Words (CBOW) with TF-IDF weighting.
The optimal choice is the Skip-gram architecture trained with hierarchical softmax. Skip-gram creates one training instance for every context word around a target term, so each appearance of a rare term yields several gradient updates, helping the model learn rich representations even from limited occurrences. Hierarchical softmax organizes the output layer as a Huffman tree, assigning shorter paths to frequent words and longer paths to infrequent ones. During training, updates for a rare word traverse the full path to its leaf, providing multiple parameter adjustments that empirically improve accuracy on low-frequency vocabulary. In contrast, negative sampling favors frequent words because negative examples are drawn from a frequency-skewed distribution; it is faster but tends to under-represent rare terms. TF-IDF weighting and GloVe pre-training are unrelated to Word2vec's neural optimization, and CBOW generally performs worse than Skip-gram on infrequent words.
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