# Scalable Topical Phrase Mining from Text Corpora

arXiv: https://arxiv.org/abs/1406.6312 · HTML: https://mlsanity.com/paper/1406.6312/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2014.

## Built on by (most cited first, 8 of 8)

- [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https://mlsanity.com/paper/2109.06304/) (2021)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (2022)
- [UCPhrase: Unsupervised Context-aware Quality Phrase Tagging](https://mlsanity.com/paper/2105.14078/) (2021)
- [SmartSales: Sales Script Extraction and Analysis from Sales Chatlog](https://mlsanity.com/paper/2204.08811/) (2022)
- [Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation](https://mlsanity.com/paper/2312.17349/) (2023)
- [Sensei: Self-Supervised Sensor Name Segmentation](https://mlsanity.com/paper/2101.00130/) (2021)
- [More Than Words: Collocation Tokenization for Latent Dirichlet Allocation Models](https://mlsanity.com/paper/2108.10755/) (2021)
- [Submission-Aware Reviewer Profiling for Reviewer Recommender System](https://mlsanity.com/paper/2211.04194/) (2022)

## Abstract (arXiv)

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