bootstrap_similarity

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

bootstrap_similarity function

<dl><dt>target_embeddings</dt>
<dd>the target embeddings (embeddings of context)</dd>
<dt>pre_trained</dt>
<dd>a V x D matrix of numeric values - pretrained embeddings with V = size of vocabulary and D = embedding dimensions</dd>
<dt>candidates</dt>
<dd>character vector defining the candidates for nearest neighbors - e.g. output from <code>get_local_vocab</code></dd>
<dt>norm</dt>
<dd>character = c("l2", "none") - set to 'l2' for cosine similarity and to 'none' for inner product (see ?sim2 in text2vec)</dd></dl>

Arguments

Boostrap similarity vector — bootstrap_similarity

<dl>

<dt>target_embeddings</dt>
<dd>the target embeddings (embeddings of context)</dd>


<dt>pre_trained</dt>
<dd>a V x D matrix of numeric values - pretrained embeddings with V = size of vocabulary and D = embedding dimensions</dd>


<dt>candidates</dt>
<dd>character vector defining the candidates for nearest neighbors - e.g. output from <code>get_local_vocab</code></dd>


<dt>norm</dt>
<dd>character = c("l2", "none") - set to 'l2' for cosine similarity and to 'none' for inner product (see ?sim2 in text2vec)</dd>

</dl>

bootstrap_similarity: Boostrap similarity vector

Description

Usage

Value

Arguments