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While most topic modeling algorithms model text corpora with unigrams, humaninterpretation often relies on inherent grouping of terms into phrases. Assuch, we consider the problem of discovering topical phrases of mixed lengths.Existing work either performs post processing to the inference results ofunigram-based topic models, or utilizes complex n-gram-discovery topic models.These methods generally produce low-quality topical phrases or suffer from poorscalability on even moderately-sized datasets. We propose a different approachthat is both computationally efficient and effective. Our solution combines anovel phrase mining framework to segment a document into single and multi-wordphrases, and a new topic model that operates on the induced document partition.Our approach discovers high quality topical phrases with negligible extra costto the bag-of-words topic model in a variety of datasets including researchpublication titles, abstracts, reviews, and news articles.

Scalable Topical Phrase Mining from Text Corpora