Name
Title | ||
|---|---|---|
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICDM.2007.86 | ICDM |
Keywords | DocType | ISSN |
topical n-grams,special meaning phrase,topic discovery,word sampling,interpretable topic,textual order,information retrieval,topic-specific bigram distribution,bigram distribution,phrase/topic discovery,text mining task,topic-specific unigram,data mining,topic-specific unigram distribution,sampling methods,topic model,text analysis,text mining,probability,probabilistic model,word order,latent dirichlet allocation,bag of words | Conference | 1550-4786 |
ISBN | Citations | PageRank |
978-0-7695-3018-5 | 197 | 6.47 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuerui Wang | 1 | 1735 | 123.38 |
| Andrew Kachites McCallumzy | 2 | 19203 | 1588.22 |
| Xing Wei | 3 | 1141 | 60.87 |