Certain common lexical attributes such as polarity and formality are continuous, creating challenges for accurate lexicon creation. Here we present a general method for automatically placing words on these spectra, using co-occurrence profiles, counts of co-occurring words within a large corpus, as a feature vector to a supervised ranking algorithm. With regards to both polarity and formality, we show this method consistently outperforms commonly-used alternatives, both with respect to the intrinsic quality of the lexicon and also when these newly-built lexicons are used in downstream tasks.
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