find_cos_sim

Find cosine similarities between target and candidate words

A fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the 'a la carte'
embeddings approach developed by Khodak et al. (2018) <arXiv:1805.05388> and evaluate hypotheses about covariate effects on embeddings using
the regression framework developed by Rodriguez et al. (2021)<https://github.com/prodriguezsosa/EmbeddingRegression>.

Pedro L. Rodriguez

conText

'a la Carte' on Text (ConText) Embedding Regression

Arthur Spirling

Brandon Stewart

Christopher Barrie

find_cos_sim function

<dl><dt>target_embedding</dt>
<dd>matrix of numeric values</dd>
<dt>pre_trained</dt>
<dd>matrix of numeric values - pretrained embeddings</dd>
<dt>candidates</dt>
<dd>character vector defining vocabulary to subset comparison to</dd>
<dt>norm</dt>
<dd>character = c("l2", "none") - how to scale input matrices. If they are already scaled - use "none" (see ?sim2)</dd></dl>

Arguments

Find cosine similarities between target and candidate words — find_cos_sim

<dl>

<dt>target_embedding</dt>
<dd>matrix of numeric values</dd>


<dt>pre_trained</dt>
<dd>matrix of numeric values - pretrained embeddings</dd>


<dt>candidates</dt>
<dd>character vector defining vocabulary to subset comparison to</dd>


<dt>norm</dt>
<dd>character = c("l2", "none") - how to scale input matrices. If they are already scaled - use "none" (see ?sim2)</dd>

</dl>

find_cos_sim: Find cosine similarities between target and candidate words

Description

Usage

Value

Arguments