Scalable Topical Phrase Mining from Text Corpora
Summary: Introduces scalable topical phrase mining by combining a phrase-mining stage with a partition-based topic model. It outperforms unigram-only methods and costly n-gram models, delivering high-quality topical phrases with negligible cost across corpora. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ahmed El-Kishky
- 2. Yanglei Song
- 3. Chi Wang
- 4. Clare R. Voss
- 5. Jiawei Han
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,913 | Mining Quality Phrases from Massive Text Corpora | 2015 | SIGMOD | 4.6139203e-05 |
| 9,136 | TextCube: Automated Construction and Multidimensional Exploration | 2019 | VLDB | 4.3843441e-05 |
| 11,783 | Building Structured Databases of Factual Knowledge from Massive Text Corpora | 2017 | SIGMOD | 4.1905499e-05 |
| 11,855 | Automatic Entity Recognition and Typing in Massive Text Data | 2016 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 182 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036955562 |
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