normalize

L2-normalization (scaling to unit euclidean length):
the norm of each vector in the vector space will be normalized to 1.
It is necessary for any linear operation of word vectors.
R code:<ul>
<li>Vector: <code>vec / sqrt(sum(vec^2))</code></li>
<li>Matrix: <code>mat / sqrt(rowSums(mat^2))</code></li>
</ul>

An integrative toolbox of word embedding research that provides:
(1) a collection of 'pre-trained' static word vectors in the '.RData'
compressed format <https://psychbruce.github.io/WordVector_RData.pdf>;
(2) a group of functions to process, analyze, and visualize word vectors;
(3) a range of tests to examine conceptual associations, including
the Word Embedding Association Test <doi:10.1126/science.aal4230>
and the Relative Norm Distance <doi:10.1073/pnas.1720347115>,
with permutation test of significance; and
(4) a set of training methods to locally train (static) word vectors
from text corpora, including 'Word2Vec' <doi:10.48550/arXiv.1301.3781>,
'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <doi:10.48550/arXiv.1607.04606>.

Han-Wu-Shuang Bao

PsychWordVec

Word Embedding Research Framework for Psychological Science

normalize function

<dl><dt>x</dt>
<dd>A <code>wordvec</code> (data.table) or
<code>embed</code> (matrix),
see <code>data_wordvec_load</code>.</dd></dl>

Arguments

Download pre-trained word vectors data (<code>.RData</code>):
<a href="https://psychbruce.github.io/WordVector_RData.pdf">https://psychbruce.github.io/WordVector_RData.pdf</a>

Download

Normalize all word vectors to the unit length 1. — normalize

<dl>

<dt>x</dt>
<dd>A <code>wordvec</code> (data.table) or
<code>embed</code> (matrix),
see <code>data_wordvec_load</code>.</dd>

</dl>

Download pre-trained word vectors data (<code>.RData</code>):
<a href='https://psychbruce.github.io/WordVector_RData.pdf'>https://psychbruce.github.io/WordVector_RData.pdf</a>

normalize: Normalize all word vectors to the unit length 1.

Description

Usage

Value

Arguments

Download

See Also

Examples