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 (University of Illinois Urbana-Champaign)
- 2. Yanglei Song (University of Illinois Urbana-Champaign)
- 3. Chi Wang (Microsoft)
- 4. Clare R. Voss (Army Research Laboratory)
- 5. Jiawei Han (University of Illinois Urbana-Champaign)
BibTeX Citation
@article{elkishky_vldb15,
title = {{Scalable Topical Phrase Mining from Text Corpora}},
author = {El-Kishky, Ahmed and Song, Yanglei and Wang, Chi and Voss, Clare R. and Han, Jiawei},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {3},
pages = {305},
doi = {10.14778/2735508.2735519},
url = {https://doi.org/10.14778/2735508.2735519},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,067 | Mining Quality Phrases from Massive Text Corpora | 2015 | SIGMOD | 5.4941436e-05 |
| 9,294 | TextCube: Automated Construction and Multidimensional Exploration | 2019 | VLDB | 5.2906228e-05 |
| 11,980 | Building Structured Databases of Factual Knowledge from Massive Text Corpora | 2017 | SIGMOD | 5.093636e-05 |
| 12,048 | Automatic Entity Recognition and Typing in Massive Text Data | 2016 | SIGMOD | 5.093636e-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 |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 161 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00027981772 |
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