Sentence examples for model for text from inspiring English sources

Mihalcea and Tarau [30] propose TextRank, a graph-based ranking model, for text processing such as keyword and sentence extraction.

For the body text, all words in the email are used to build the PLSA model for text view.

Other than the Naive Bayes algorithm, some scholars have also used the maximum entropy model for text classification (Zhang and Swanson 2010).

The learning design of ALMA is based on Kintsch's Construction-Integration model for text comprehension and takes into account the learner's background knowledge in order to propose a text of appropriate cohesion from four versions of a text, orthogonally varying local and global cohesion.

It is not the first time that a generative model for text has walked parallel paths with models of network growth.

The topic model – a recently-developed Bayesian model for text document collections [38] – is considered a state-of-the-art algorithm for extracting semantic structure from text collections.

Using this design, a statistical model for texts and a new collection of news stories, we show that media outlets allocate substantially more attention to terrorism after an alert.

The topic modeling for text collection using LDA is performed in four steps.

First mentioned entities have a special status in event models for text narratives (e.g., Gernsbacher, 1990).

In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.

"Latent topic modeling for text analysis" presents methodological details underlying non-negative matrix factorization as a method for topic modeling (Lee and Seung 1999).