The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
Open accessAbstract
Most topic models, such as latent Dirichlet allocation, rely on the bag-of-words assumption. However, word order and phrases are often critical to capturing the meaning of text in many text mining tasks. This paper presents topical n-grams, a topic model that discovers topics as well as topical phrases. The probabilistic model generates words in their textual order by, for each word, first sampling a topic, then sampling its status as a unigram or bigram, and then sampling the word from a topic-specific unigram or bigram distribution. Thus our model can model "white house" as a special meaning phrase in the 'politics' topic, but not in the 'real estate' topic. Successive bigrams form longer phrases. We present experiments showing meaningful phrases and more interpretable topics from the NIPS data and improved information retrieval performance on a TREC collection.
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Venugopalan, S., & Rai, V. (2015) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2014.10.006 · Cited by 116
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Metadata
- Authors
- Wang, X., McCallum, A., & Wei, X.
- Venue
- Seventh IEEE International Conference on Data Mining (ICDM 2007)
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 697-702
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2169606435
- Cited by
- 482
- References
- 21
- FWCI
- 8.67
- Type
- conference paper
- Language
- en
- Open access
- green
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