You are tasked with building word embeddings for a biomedical text-mining system. The corpus contains many domain-specific compound words that appear only once (for example, "interferon-beta-1a"), yet researchers still want to query the vectors with arithmetic analogies such as "ribosome − protein + RNA ≈ ?". Which embedding approach most directly meets the dual requirement of (1) assigning informative vectors to these low-frequency or unseen tokens and (2) preserving the linear relationships exploited by analogy tasks, without fine-tuning a large language model?
Factorize a global word-word co-occurrence matrix with GloVe to obtain dense vectors.
Train a skip-gram Word2vec model with negative sampling on word tokens only.
Use fastText to learn subword-level skip-gram embeddings that compose each word vector from its character n-grams.
Create one-hot vectors for every word in the corpus and apply principal component analysis to reduce their dimensionality.
fastText learns skip-gram embeddings in which each word vector is the sum of its character n-gram vectors. Because the model shares subword components, it can generate meaningful representations for rare or even unseen words, a crucial feature when the vocabulary includes biomedical compounds that appear only once. The skip-gram training objective keeps the same linear geometry as Word2vec, so vector arithmetic for analogies still works. A vanilla Word2vec skip-gram model improves over CBOW for rare words but cannot infer vectors for out-of-vocabulary tokens. GloVe similarly assigns one vector per surface form and cannot compose embeddings for unseen words at inference time. Reducing one-hot encodings with PCA captures no distributional semantics and offers no guarantee of useful analogy structure.
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Why is fastText able to generate meaningful embeddings for unseen or rare words?
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How does fastText maintain linear relationships for analogy tasks?
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Why do approaches like Word2vec or GloVe fail to handle unseen words effectively?