Sentence examples for methods for topic from inspiring English sources

In the following subsection, we briefly discuss some well known methods for topic discovery such as, association rules, latent semantics, and latent Dirichlet allocation.

Recent research, however, has built connections between linear algebraic and probabilistic methods for topic modeling (Arora et al. 2013), supporting the usefulness of linear algebraic methods as an efficient way to compute topic models.

Implement and study novel methods for topic-specific or vertical information discovery and search, as well as for high-quality persistent search.

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

In addition, we use a cotagging method for topic discovery as we think that it suits multimedia crawling more than the traditional taxonomy methods do, because it can help to discover some hidden and dynamic tag relations that may not be encoded in a rigid taxonomy (e.g., "tree" and "sky" may be related in many sets of scenery photos).

Here we also introduce methods for evaluating topic model classifications using the official classifications as a benchmark.

We devised methods for categorising topics as there was little guidance from JLA for carrying out this aspect of the process; however, these methods require further refinement.

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways.

In contrast to quantitative research that constitutes an abstract representation of a real situation leading to criticism of its representational accuracy of complexity (i.e., complexity reduction approach), qualitative research emphasizes an understanding of relationships within complexity is a more appropriate method for this topic (i.e., complexity absorption approach).

The goal of the current section is to describe methods for building latent topic models using text-based descriptions of crimes.

Specifically, we sought to examine students' preferences on timing, setting, and pedagogical methods for teaching these topics.