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.

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

Details

See Also

Examples