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

By Xuerui Wang, Andrew McCallum, Xing Wei. Industrial Conference on Data Mining, 2007

541 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Evaluating the use of search engine development tools in IT education (2010, unknown): “For example, in text retrieval, the considerations of two adjacent terms [10] [ 11 ] and two nearby terms (e.g.”
Topic Modeling Using Latent Dirichlet allocation (2021, background): “proposed an n-gram topic model framework for learning not only topics but phrases also [150].”
"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention (2018, methodology): “We independently utilized LDA over subreddits and subreddit bigrams, and generated 110 sub-topics that comprises the top 2 sub-topics from the 55 topics [38].”
Topic and Role Discovery in Social Networks (2005, background): “... have been actively studied in recent years for many dierent tasks, including joint modeling of words and research paper citations (Erosheva, Fienberg, & Laerty, 2004), capturing correlations among topics (Blei & Laerty, 2006a; Li & McCallum, 2006), taking advantage of both topical and syntactic ”
Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality (2014, methodology): “…(LDA: Blei et al. (2003)) and related methods is increasingly being used in user-focused tasks, in contexts such as the evaluation of scientiﬁc impact (McCallum et al., 2006; Hall et al., 2008), trend analysis (Bolelli et al., 2009; Lau et al., 2012a) and document search (Wang et al., 2007).”