  This article presents a probabilistic generative model for text based on
semantic topics and syntactic classes called Part-of-Speech LDA (POSLDA).
POSLDA simultaneously uncovers short-range syntactic patterns (syntax) and
long-range semantic patterns (topics) that exist in document collections. This
results in word distributions that are specific to both topics (sports,
education, ...) and parts-of-speech (nouns, verbs, ...). For example,
multinomial distributions over words are uncovered that can be understood as
"nouns about weather" or "verbs about law". We describe the model and an
approximate inference algorithm and then demonstrate the quality of the learned
topics both qualitatively and quantitatively. Then, we discuss an NLP
application where the output of POSLDA can lead to strong improvements in
quality: unsupervised part-of-speech tagging. We describe algorithms for this
task that make use of POSLDA-learned distributions that result in improved
performance beyond the state of the art.
