Sentence examples for models for text from inspiring English sources

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.

Such selection can help in building effective and efficient models for text classification.

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

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

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

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

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

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.

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.

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