DTM.add_ngrams: Find non-consecutive n-grams
In bakaburg1/BaySREn: BaySREn. An R package to automatise citation collection and screening in Systematic Reviews. Based on Bayesian active machine learning
DTM.add_ngrams R Documentation
Find non-consecutive n-grams
Description
Build a term-term network using a cosine similarity measure built on the term
co-presence in documents. A threshold defined in min.sim is used to
identify edges. The maximal cliques of the network represent the discovered
n-grams.
Usage
DTM.add_ngrams(DTM, min.sim = 0.5, max.terms = 10)
Arguments
DTM
A Document Term Matrix.
min.sim
The minimal cosine similarity that identifies an edge.
max.terms
The maximum size (i.e., the number of terms) in an n-gram.
Value
The same input Document Term Matrix with extra columns for the
n-grams.
bakaburg1/BaySREn documentation built on March 30, 2022, 12:16 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| DTM.add_ngrams | R Documentation |
Find non-consecutive n-grams
Description
Build a term-term network using a cosine similarity measure built on the term
co-presence in documents. A threshold defined in min.sim is used to
identify edges. The maximal cliques of the network represent the discovered
n-grams.
Usage
DTM.add_ngrams(DTM, min.sim = 0.5, max.terms = 10)
Arguments
DTM |
A Document Term Matrix. |
min.sim |
The minimal cosine similarity that identifies an edge. |
max.terms |
The maximum size (i.e., the number of terms) in an n-gram. |
Value
The same input Document Term Matrix with extra columns for the n-grams.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.