similarity_matrix

This function takes a fitted word embedding model and computes the cosine similarity between
each word.

keyclust

A fast and computationally efficient algorithm designed to enable researchers to efficiently and quickly extract semantically-related keywords using a fitted embedding model. For more details about the methods applied, see Chester (2025). <doi:10.17605/OSF.IO/5B7RQ>.

Patrick Chester

A Model for Semi-Supervised Keyword Extraction from Word
Embedding Models

similarity_matrix function

<dl><dt>x</dt>
<dd>A word embedding matrix</dd>
<dt>words</dt>
<dd>A vector of words or the name of a column that corresponds to the word dimension of the fitted word embeddings</dd>
<dt>max_terms</dt>
<dd>The maximum number of embedding terms that will be included in output similarity matrix.
Assumes that embedding input is ordered by word frequency.</dd></dl>

Arguments

Algorithm designed to create a cosine similarity matrix from a fitted word embedding model — similarity_matrix

<dl>

<dt>x</dt>
<dd>A word embedding matrix</dd>


<dt>words</dt>
<dd>A vector of words or the name of a column that corresponds to the word dimension of the fitted word embeddings</dd>


<dt>max_terms</dt>
<dd>The maximum number of embedding terms that will be included in output similarity matrix.
Assumes that embedding input is ordered by word frequency.</dd>

</dl>

similarity_matrix: Algorithm designed to create a cosine similarity matrix from a fitted word embedding model

Description

Usage

Value

Arguments

Examples