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原文摘要

Static word embeddings give a word one vector, ignoring its different functions as noun, verb or adjective. Training on part-of-speech-tagged text gives each function its own vector, at the cost of a larger table. Using nine controlled corpora, we ask which context words each part of speech needs, whether it needs its own subspace, and whether the number of dimensions a word keeps should follow its part of speech or its frequency. The tags raise verb similarity on SimVerb-3500 at every dimension from 50 to 300 but help nouns much less. Added to verb-only text, adverbs help verbs more than as many random words; adjectives help nouns only slightly. At a fixed dimension, per-part-of-speech PCA bases preserve verb and adjective similarity better than a global basis or frequency-band bases. Nouns need more dimensions than verbs only among frequent words. We therefore allocate dimensions by frequency with these bases: at a third of the original size, only rare words lose dimensions, SimVerb-3500 and SimLex-999 nouns match the uncompressed table, and verb-to-noun retrieval stays within 0.02. Part of speech decides what a word's vector should be; frequency decides how much of it to keep.