Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

@article{Wang2007TopicalNP,
  title={Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval},
  author={Xuerui Wang and Andrew McCallum and Xing Wei},
  journal={Seventh IEEE International Conference on Data Mining (ICDM 2007)},
  year={2007},
  pages={697-702}
}
Most topic models, such as latent Dirichlet allocation, rely on the bag-of-words assumption. [] Key Method 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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512 Citations
Highly Influential Citations
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Background Citations
229
Methods Citations
149
Results Citations
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512 Citations

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A Phrase Topic Model for Large-scale Corpus

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Review Topic Discovery with Phrases using the Pólya Urn Model

This paper proposes to use the generalized Polya Urn (GPU) model to solve the topic modelling problem, which gives superior results and enables the connection of a phrase with its content words naturally.

Enhancing Topical Word Semantic for Relevance Feature Selection

An innovative and effective extended random sets (ERS) model is presented to enhance the semantic of topical words and significantly outperforms eight, state-of-the-art, baseline models in five standard performance measures.

LDA-PSTR: A Topic Modeling Method for Short Text

This paper applies frequent pattern mining to uncover statistically significant patterns which can explicitly capture semantic association and co-occurrences among corpus-level words, and proposes a new probabilistic topic model called LDA-PSTR.

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

SenU-PTM reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.

Discovering latent topical phrases in document collections and networks with text components: Leveraging text mining and information network analysis for human oriented applications

A framework that generates high-quality topics represented by integrated lists of mixed-length phrases, and an approach to constructing hierarchical topics, which extends the phrase-centric approach to create high quality phrases at varying levels of granularity are described.

Bigram Anchor Words Topic Model

This paper offers an approach to accounting bigrams (two-word phrases) for the construction of Anchor Words Topic Model, a probabilistic topic model that allows extracting a number of topics in the collection and describes each document as a discrete probability distribution over topics.

Personalized Multi-Document Summarization using N-Gram Topic Model Fusion

A unified topic model which evolves from sentence-term and sentence-bigram co-occurrences in parallel is presented, built on a considerably simpler model than previous topic modeling approaches to summarization.

Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

This paper proposes a novel topic model, called Labeled Phrase Latent Dirichlet Allocation (LPLDA), which regards each document as a mixture of phrases and partly considers the word order, and develops a batch inference algorithm based on Gibbs sampling technique for LPLDA.
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