conText was designed to work closely with quanteda, as such, most functions will expect a quanteda object such as a tokens or dfm or fcm object. To make the most of conText, we recommend that you familiarize yourself with some of quanteda’s basic functionalities (see Quanteda Quick Start Guide).
You can install the package directly from CRAN:
install.packages("conText")Or, for the latest development version, from GitHub:
devtools::install_github("prodriguezsosa/conText")library(conText)
# other libraries used in this guide
library(quanteda)
library(dplyr)
library(text2vec)To use conText you will need three objects:
In this guide we will use the sample objects included in the package:
cr_sample_corpus, cr_glove_subset and
cr_transform. Note, these are only meant to illustrate
function implementations, keep in mind their (small) size when
interepreting results. We’ve made available the full versions of these
objects here.
Pre-processing is always context-specific (no pun intended). We
generally recommend not “overly” pre-processing a corpus. Below we
outline how a typical pre-processing pipeline might look. Note,
cr_sample_corpus already has some minor pre-processing so
some of these steps may be redundant but are left for completeness. It
is generally a good idea to set padding = TRUE in
tokens_select() to avoid making non-adjacent words adjacent
prior to computing the embeddings. This is increasingly important as
more features are removed.
# tokenize corpus removing unnecessary (i.e. semantically uninformative) elements
toks <- tokens(cr_sample_corpus, remove_punct=T, remove_symbols=T, remove_numbers=T, remove_separators=T)
# clean out stopwords and words with 2 or fewer characters
toks_nostop <- tokens_select(toks, pattern = stopwords("en"), selection = "remove", min_nchar=3)
# only use features that appear at least 5 times in the corpus
feats <- dfm(toks_nostop, tolower=T, verbose = FALSE) %>% dfm_trim(min_termfreq = 5) %>% featnames()
# leave the pads so that non-adjacent words will not become adjacent
toks_nostop_feats <- tokens_select(toks_nostop, feats, padding = TRUE)Suppose we are interested in the semantics surrounding the word “immigration” in the U.S. Congress during the Obama years (sessions 111th - 114th).
We begin by identifying all instances of the target term
–“immigration”– in our corpus and, for each instance, store it’s
context– the N words (N = 6 in this example) preceding and following the
instance, and tokenize. Given our tokenized corpus,
toks_nostop_feats, we can do this in one step using
conText::tokens_context() –a wrapper function for
quanteda::kwic(). Notice, both the input, x, and the output
are a quanteda tokens objects. Each document in
immig_toks –tokens of a context around an instance of
immigration– inherits the document variables
(docvars) of the document from whence it came, along with a
column registering the corresponding pattern. This information can be
retrieved using docvars().
# build a tokenized corpus of contexts sorrounding the target term "immigration"
immig_toks <- tokens_context(x = toks_nostop_feats, pattern = "immigr*", window = 6L)## 125 instances of "immigrant" found.
## 288 instances of "immigrants" found.
## 924 instances of "immigration" found.head(docvars(immig_toks), 3)## pattern party gender nominate_dim1
## 1 immigrants D F -0.759
## 2 immigration D F -0.402
## 3 immigrant D F -0.402Given a tokenized corpus of contexts, we next build it’s
corresponding document-feature-matrix, namely a matrix where each row
represents a given document’s vector of feature counts. We do this using
quanteda’s dfm() function.
# build document-feature matrix
immig_dfm <- dfm(immig_toks)
immig_dfm[1:3,1:3]## Document-feature matrix of: 3 documents, 3 features (66.67% sparse) and 4 docvars.
## features
## docs institutions moral stood
## text1 1 1 1
## text2 0 0 0
## text3 0 0 0Given a ‘dfm’, immig_dfm, a set of pre-trained
embeddings, cr_glove_subset and a corresponding
transformation matrix, cr_transform, we can proceed to
embed each document –i.e. context– ‘a la carte’. We embed a
document by multiplying each of it’s feature counts with their
corresponding pre-trained feature-embeddings, column-averaging the
resulting vectors, and multiplying by the transformation matrix. This
“transforms” a sparse V-dimensional vector (a vector of feature
counts, with V = number of features in the corpus) into a dense
D-dimensional vector (a D-dimensional embedding, with D = dimensions of
the pre-trained embeddings). We do this using
conText::dem()–‘dem’ standing for
document-embedding-matrix. Each row in this matrix represents
an ‘a la carte’ (ALC) embedding of a single instance of
“immigration”.
Keep in mind, only those features that appear in the set of
pre-trained embeddings will be used in computing a document’s embedding.
Documents with no features overlapping with the pre-trained provided are
dropped. dem() –outputs a ‘dem-class’ object which is
similar in many ways to ‘dfm-class’ object. Importantly, a ‘dem-class’
object inherits all the documenta variables, ‘docvars’, from the ‘dfm’
used to compute it (except those of documents that could not be
embedded). Additionally, a ‘dem-class’ object will store other useful
attributes, including the names of the documents that were embedded and
the vector of features used.
# build a document-embedding-matrix
immig_dem <- dem(x = immig_dfm, pre_trained = cr_glove_subset, transform = TRUE, transform_matrix = cr_transform, verbose = TRUE)
# each document inherits its corresponding docvars
#head(immig_dem@docvars)
# you can check which documents were not embedded due to lack of overlapping features (in this example all documents are embedded)
# note: 'quanteda' functions like `docvars()` and `docnames()` don't work on `dem` objects, so you will have to call the attributes directly.
#setdiff(docnames(immig_dfm), immig_dem@Dimnames$docs)
# vector of features used to create the embeddings
#head(immig_dem@features)We now have an ALC embedding for each instance of “immigration” in our sample corpus. To get a single corpus-wide ALC embedding for “immigration”, we can simply take the column-average of the single-instance ALC embeddings.
# to get a single "corpus-wide" embedding, take the column average
immig_wv <- matrix(colMeans(immig_dem), ncol = ncol(immig_dem)) %>% `rownames<-`("immigration")
dim(immig_wv)## [1] 1 300However, we are usually interested in exploring semantic differences
across groups. To do so, we can average using a grouping variable
defined by one or a combination of the ‘docvars’. We do this using
conText::dem_group() (very similar in flavor to quanteda’s
dfm_group()).
In our example below, this results in an ALC embedding of “immigration”
for each party, hence the dimensions \(2\) by \(300\).
# to get group-specific embeddings, average within party
immig_wv_party <- dem_group(immig_dem, groups = immig_dem@docvars$party)
dim(immig_wv_party)## [1] 2 300Given an ALC embedding for each party, we can proceed to do some exploratory analysis.
First, we can evaluate differences in nearest neighbors –features
with the highest cosine-similarity with each group embedding– using
conText::nns(). We use the candidates argument
to limit the set of features we want nns to consider as
candidate nearest neighbors. In this example we limit candidates to
those features that appear in our corpus (otherwise, any word in the set
of pre-trained embeddings can be a candidate).
# find nearest neighbors by party
# setting as_list = FALSE combines each group's results into a single tibble (useful for joint plotting)
immig_nns <- nns(immig_wv_party, pre_trained = cr_glove_subset, N = 5, candidates = immig_wv_party@features, as_list = TRUE)
# check out results for Republican party
immig_nns[["R"]]## # A tibble: 5 × 4
## target feature rank value
## <chr> <chr> <int> <dbl>
## 1 R immigration 1 0.843
## 2 R illegal 2 0.768
## 3 R immigrants 3 0.732
## 4 R illegally 4 0.664
## 5 R amnesty 5 0.658We can also look at the relationship between each group’s ALC
embedding of “immigration” and a specific set of features. For example,
how does each party’s understanding of immigration compare on the
“reform” vs. “enforcement” dimension? To answer this, we use
conText::cos_sim(). Results (on this limited sample of the
data) suggest Democrats are more prone to speak of “reform” in the
context of “immigration” whereas Republicans are more likely to speak of
“enforcement”.
# compute the cosine similarity between each party's embedding and a specific set of features
cos_sim(immig_wv_party, pre_trained = cr_glove_subset, features = c('reform', 'enforcement'), as_list = FALSE)## target feature value
## 1 D reform 0.5949757
## 2 R reform 0.4270958
## 3 D enforcement 0.6059709
## 4 R enforcement 0.5968879A third exploratory function included in ‘conText’ is
nns_ratio(). Given ALC embeddings for two groups,
nns_ratio() computes the ratio of cosine similarities
between group embeddings and features –that is, for any given feature it
first computes the similarity between that feature and each group
embedding, and then takes the ratio of these two similarities. This
ratio captures how “discriminant” a feature is of a given group. Values
larger (smaller) than \(1\) mean the
feature is more (less) discriminant of the group in the numerator
(denominator). Use the numerator argument to define which
group represents the numerator in this ratio. If N is
defined, this ratio is computed for union of the top N
nearest neighbors.
# compute the cosine similarity between each party's embedding and a specific set of features
nns_ratio(x = immig_wv_party, N = 10, numerator = "R", candidates = immig_wv_party@features, pre_trained = cr_glove_subset, verbose = FALSE)## feature value
## 1 enforce 1.2076754
## 2 illegal 1.1834716
## 3 amnesty 1.1716428
## 4 illegally 1.1232888
## 5 laws 1.0893494
## 6 legal 1.0261909
## 7 immigrants 1.0070933
## 8 border 1.0043546
## 9 enforcement 0.9850108
## 10 immigration 0.9809758
## 11 immigrant 0.9118817
## 12 undocumented 0.9008638
## 13 broken 0.7631982
## 14 comprehensive 0.7248641
## 15 reform 0.7178373We are used to hearing about “nearet neighbors” when it comes to
interpreting word embeddings. However, it is often the case that nearest
neighbors –i.e. single words– without any context are hard to interpret.
For this reason we introduce “nearest contexts” – contexts around a
target term that are semantically close –i.e. high cosine similarity– to
the ALC embedding of that term. To explore nearest contexts we use the
ncs() function. ncs() computes the cosine
similarities between the ALC (group) embeddings and the single-instance
ALC embeddings of the underlying contexts.
# compute the cosine similarity between each party's embedding and a set of tokenized contexts
immig_ncs <- ncs(x = immig_wv_party, contexts_dem = immig_dem, contexts = immig_toks, N = 5, as_list = TRUE)
# nearest contexts to Republican embedding of target term
# note, these may included contexts originating from Democrat speakers
immig_ncs[["R"]]## # A tibble: 5 × 4
## target context rank value
## <chr> <chr> <int> <dbl>
## 1 R america suggest immigration can good thing illegal immigra… 1 0.817
## 2 R immigration can good thing immigration illegal legal immig… 2 0.787
## 3 R immigration law prior illegal immigration reform responsib… 3 0.785
## 4 R good thing immigration illegal immigration legal mean mean… 4 0.773
## 5 R going cost hardworking taxpayers america suggest can good … 5 0.758# you can limit candidate contexts to those of a specific party
immig_ncs <- ncs(x = immig_wv_party["R",], contexts_dem = immig_dem[immig_dem@docvars$party == "R",], contexts = immig_toks, N = 5, as_list = FALSE)All functions to explore nearest neighbors –nns,
cos_sim, nns_ratio– have to option to add
stemming. This can be useful to group nearest neighbors with the same
stem e.g. “reform”, “reforms”, “reformed”. Under the hood, candidate
nearest neighbors are stemmed using the Snowball library
and cosine similarities with the target embeddings are averaged across
nearest neighbors with the same stem. To avoid noisy words influencing
this average e.g. “reformedthesystem”, we recommend you remove
misspelled words from the candidate set candidates (you can
automate this using the hunspell library). See example
below.
# extract candidate features from the dem object
immig_feats <- immig_wv_party@features
# check spelling. toupper avoids names being considered misspelled
if (requireNamespace("hunspell", quietly = TRUE)) {
library(hunspell) # spellcheck library
spellcheck <- hunspell_check(toupper(immig_feats), dict = hunspell::dictionary("en_US"))
immig_feats <- immig_feats[spellcheck]
}##
## Attaching package: 'hunspell'
## The following object is masked from 'package:quanteda':
##
## dictionary# find nearest neighbors by party using stemming
immig_nns_stem <- nns(immig_wv_party, pre_trained = cr_glove_subset, N = 5, candidates = immig_feats, stem = TRUE, as_list = TRUE)## Using porter for stemming. To check available languages run "SnowballC::getStemLanguages()"# check out results for Republican party
immig_nns_stem[["R"]]## # A tibble: 5 × 4
## target feature rank value
## <chr> <chr> <int> <dbl>
## 1 R illeg 1 0.716
## 2 R immigr 2 0.704
## 3 R amnesti 3 0.658
## 4 R enforc 4 0.593
## 5 R law 5 0.578In the above example we had one target word, “immigration”. However,
we can also explore the semantics of multiple targets simultaneously,
including phrases! We simply provide a vector of patterns in the
pattern argument of tokens_context().
# build a corpus of contexts sorrounding the target term "immigration"
mkws_toks <- tokens_context(x = toks_nostop_feats, pattern = c("immigration", "welfare", "immigration reform", "economy"), window = 6L, verbose = FALSE)
# create document-feature matrix
mkws_dfm <- dfm(mkws_toks)
# create document-embedding matrix using a la carte
mkws_dem <- dem(x = mkws_dfm, pre_trained = cr_glove_subset, transform = TRUE, transform_matrix = cr_transform, verbose = FALSE)
# get embeddings for each pattern
mkws_wvs <- dem_group(mkws_dem, groups = mkws_dem@docvars$pattern)
# find nearest neighbors for each keyword
mkws_nns <- nns(mkws_wvs, pre_trained = cr_glove_subset, N = 5, candidates = mkws_wvs@features, as_list = TRUE)
# to check results for a given pattern
mkws_nns[["immigration reform"]]## # A tibble: 5 × 4
## target feature rank value
## <chr> <chr> <int> <dbl>
## 1 immigration reform comprehensive 1 0.817
## 2 immigration reform immigration 2 0.739
## 3 immigration reform reform 3 0.635
## 4 immigration reform pass 4 0.609
## 5 immigration reform bipartisan 5 0.581This can also be useful if we wanted to explore group differences in the semantics of a given topic where we define a topic by a vector of topical target words. Note, these topical embeddings are computed off of the collection of contexts around the set of pattern words provided. So, in the example below, the contexts around each of “immigration”, “immigrant” and “immigration reform” are treated as a single collection of contexts for the purposes of computing each party’s topical embedding.
# build a corpus of contexts sorrounding the immigration related words
topical_toks <- tokens_context(x = toks_nostop_feats, pattern = c("immigration", "immigrant", "immigration reform"), window = 6L, verbose = FALSE)
# create document-feature matrix
topical_dfm <- dfm(topical_toks)
# create document-embedding matrix using a la carte
topical_dem <- dem(x = topical_dfm, pre_trained = cr_glove_subset, transform = TRUE, transform_matrix = cr_transform, verbose = FALSE)
# get "topical" embeddings for each party
topical_wvs <- dem_group(topical_dem, groups = topical_dem@docvars$party)
# find nearest neighbors for each keyword
nns(topical_wvs, pre_trained = cr_glove_subset, N = 5, candidates = topical_wvs@features, stem = TRUE, as_list = FALSE)## Using porter for stemming. To check available languages run "SnowballC::getStemLanguages()"
## # A tibble: 10 × 4
## target feature rank value
## <fct> <chr> <int> <dbl>
## 1 D comprehens 1 0.699
## 2 R illeg 1 0.677
## 3 R immigr 2 0.673
## 4 D broken 2 0.668
## 5 D immigr 3 0.663
## 6 D reform 4 0.661
## 7 R amnesti 3 0.659
## 8 R enforc 4 0.626
## 9 R law 5 0.609
## 10 D act 5 0.549The above functions give users a lot of flexibility, however, it can
be cumbersome to write each step every time one wants to do some
exploratory analysis on a corpus. In this section we’ll look into a
collection of wrapper functions that allow users to go straight from a
tokenized corpus to results. Each of the four exploratory analysis
functions –nns(), cos_sim(),
nns_ratio() and ncs()– described above has its
corresponding wrapper function.
An key advantage of the wrapper functions is that they make it easy
to obtain standard errors and confidence intervals around the sample
statistics of interest via bootstrapping. Bootstrapping works
by sampling with replacement documents from our tokenized corpus of
contexts (within group if the argument groups is specified)
and going through the above steps for each of our exploratory analysis
functions (i.e. computing an ‘a la carte’ embedding on each bootstrapped
sample, then computing the cosine similarities etc.). Let’s start with
conText::get_nns(), a wrapper function for
nns().
We use the same tokenized corpus of “immigration” contexts
–immig_toks– as above, and again limit candidate nearest
neighbors to the set of features in our corpus. To group by
party we set
groups = docvars(immig_corpus, 'party') –the
groups argument can include more than two groups. If no
grouping variable is provided, the full set of (tokenized) contexts are
aggregated into one single embedding. To estimate standard errors for
the cosine similarities between each party’s embedding and their
corresponding nearest neighbors we set bootstrap = TRUE and
define the desired number of bootstraps with num_bootstrap.
Notice the output now has three additional columns:
std.error –the standard deviation of the sampling
distribution of cosine similarities obtained via bootstrapping–,
lower.ci and upper.ci –the bootstrapped
confidence interval. Note, values may differ slightly to the
step-by-step process outlined above as they represent averages over
bootstrapped samples.
# we limit candidates to features in our corpus
feats <- featnames(dfm(immig_toks))
# compare nearest neighbors between groups
set.seed(2021L)
immig_party_nns <- get_nns(x = immig_toks, N = 10,
groups = docvars(immig_toks, 'party'),
candidates = feats,
pre_trained = cr_glove_subset,
transform = TRUE,
transform_matrix = cr_transform,
bootstrap = TRUE,
num_bootstraps = 100,
confidence_level = 0.95,
as_list = TRUE)## starting bootstraps
## done with bootstraps# nearest neighbors of "immigration" for Republican party
immig_party_nns[["R"]]## # A tibble: 10 × 7
## target feature rank value std.error lower.ci upper.ci
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 R immigration 1 0.838 0.0100 0.822 0.855
## 2 R illegal 2 0.762 0.0101 0.745 0.779
## 3 R immigrants 3 0.728 0.0149 0.706 0.750
## 4 R illegally 4 0.660 0.00957 0.643 0.676
## 5 R amnesty 5 0.654 0.0120 0.635 0.673
## 6 R laws 6 0.595 0.0151 0.570 0.620
## 7 R enforcement 7 0.592 0.0139 0.568 0.610
## 8 R enforce 8 0.586 0.0155 0.564 0.618
## 9 R border 9 0.568 0.0156 0.534 0.594
## 10 R legal 10 0.565 0.0134 0.541 0.583conText::get_cos_sim() is a wrapper function for
cos_sim(), used to evaluate how similar each group’s (or
single if groups is not defined) embedding is to a set of
features of interest –as with get_nns(), the
groups argument can take on more than two groups. Again we
set bootstrap = TRUE to obtain standard errors for the
cosine similarities.
# compute the cosine similarity between each group's embedding and a specific set of features
set.seed(2021L)
get_cos_sim(x = immig_toks,
groups = docvars(immig_toks, 'party'),
features = c("reform", "enforce"),
pre_trained = cr_glove_subset,
transform = TRUE,
transform_matrix = cr_transform,
bootstrap = TRUE,
num_bootstraps = 100,
as_list = FALSE)## starting bootstraps
## done with bootstraps
## # A tibble: 4 × 6
## target feature value std.error lower.ci upper.ci
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 D reform 0.591 0.0186 0.557 0.618
## 2 D enforce 0.484 0.0126 0.461 0.503
## 3 R reform 0.424 0.0169 0.396 0.451
## 4 R enforce 0.586 0.0155 0.564 0.618conText::get_nns_ratio() is a wrapper function for
nns_ratio(), used to gauge how discriminant nearest
neighbors are of each group. Unlike get_nns() and
get_cos_sim(), a groups argument must be
provided and it must be binary. As with nns_ratio(), we use
the numerator argument to control which group to use as the
numerator in the underlying ratio. We again limit the candidate nearest
neighbors to the set of local features and set
bootstrap = TRUE to get standard errors, in this case of
the cosine similarity ratios.
Finally, get_nns_ratio() allows us to make inferences
using a permutation test, specifically around the absolute deviation
of the observed cosine similarity ratio from \(1\) (this captures how discriminant a
given nearest neighbor is). Specifically, for each permutation, the
grouping variable is randomly shuffled and the absolute deviation of the
cosine similarity ratios from \(1\) is
computed. The empirical p.value is then the proportion of these
“permuted” deviations that are larger than the observed deviation.
The output of get_nns_ratio() contains three additional
columns (relative to nns_ratio()) if
bootstrap = TRUE and permute = TRUE. These are
the standard errors around the cosine similarity ratios, the
corresponding (empirical) p.value and a group
variable identifying which group the nearest neighbor belonged to –
shared means it appeared in both groups’ top \(N\) nearest neighbors.
# we limit candidates to features in our corpus
feats <- featnames(dfm(immig_toks))
# compute ratio
set.seed(2021L)
immig_nns_ratio <- get_nns_ratio(x = immig_toks,
N = 10,
groups = docvars(immig_toks, 'party'),
numerator = "R",
candidates = feats,
pre_trained = cr_glove_subset,
transform = TRUE,
transform_matrix = cr_transform,
bootstrap = TRUE,
num_bootstraps = 100,
permute = TRUE,
num_permutations = 100,
verbose = FALSE)## starting bootstraps
## done with bootstraps
## starting permutations
## done with permutationshead(immig_nns_ratio)## # A tibble: 6 × 7
## feature value std.error lower.ci upper.ci p.value group
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 enforce 1.21 0.0456 1.14 1.28 0 R
## 2 illegal 1.18 0.0289 1.13 1.23 0 shared
## 3 amnesty 1.17 0.0315 1.11 1.22 0 R
## 4 illegally 1.12 0.0290 1.08 1.18 0 shared
## 5 laws 1.09 0.0351 1.03 1.15 0 R
## 6 legal 1.03 0.0341 0.973 1.08 0.39 RconText also includes a plotting function,
plot_nns_ratio, specific to get_nns_ratio(),
providing a nice visualization of its output. alpha defines
the desired significance threshold to denote “significant” results on
the plot (indicated by a \(*\) next to
the feature). Also, you can choose between two different visualizations
of the same results using the horizontal argument.
plot_nns_ratio(x = immig_nns_ratio, alpha = 0.01, horizontal = TRUE)
conText::get_ncs() is a wrapper function for
ncs(), used to compute cosine similarities between ALC
(group) embeddings and the ALC embeddings of individual contexts with
the option to bootstrap standard errors.
# compare nearest neighbors between groups
set.seed(2021L)
immig_party_ncs <- get_ncs(x = immig_toks,
N = 10,
groups = docvars(immig_toks, 'party'),
pre_trained = cr_glove_subset,
transform = TRUE,
transform_matrix = cr_transform,
bootstrap = TRUE,
num_bootstraps = 100,
as_list = TRUE)## starting bootstraps
## done with bootstraps# nearest neighbors of "immigration" for Republican party
immig_party_ncs[["R"]]## # A tibble: 10 × 7
## target context rank value std.e…¹ lower…² upper…³
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 R america suggest immigration can g… 1 0.812 0.00956 0.796 0.828
## 2 R immigration can good thing immigr… 2 0.781 0.0101 0.765 0.799
## 3 R immigration law prior illegal imm… 3 0.780 0.0119 0.760 0.797
## 4 R good thing immigration illegal im… 4 0.768 0.00887 0.754 0.782
## 5 R going cost hardworking taxpayers … 5 0.754 0.0101 0.737 0.770
## 6 R actually increase illegal immigra… 6 0.731 0.00739 0.720 0.743
## 7 R immigration enforcement along sou… 7 0.729 0.0108 0.711 0.746
## 8 R responsible immigration policies … 8 0.721 0.0116 0.703 0.740
## 9 R late right thing hold president r… 9 0.721 0.0131 0.695 0.743
## 10 R backlog met likely lead reduction… 10 0.715 0.00989 0.696 0.730
## # … with abbreviated variable names ¹std.error, ²lower.ci, ³upper.ciThe above framework allows us to explore semantic differences over
one grouping variable at a time. However, we often want to look at
covariate effects while controlling for other covariates or indeed go
beyond discrete covariates. In Rodriguez,
Spirling and Stewart (2021) we show how a la carte
embeddings can be used within a regression-framework that allows us to
do just that. The corresponding package function is
conText::conText().
conText() tries to follow a similar syntax as R’s
lm() and glm() functions. data
must be a quanteda tokens object with covariates stored as document
variables (docvars). We next specify a formula consisting
of the target word of interest, e.g. “immigration” and the set of
covariates. To use all covariates in data, we can speficy
immigration ~ .. formula can also take vectors
of target words
e.g. c("immigration", "immigrants") ~ party + gender and
phrases e.g. "immigration reform" ~ party + gender – place
phrases in quotation marks.
# two factor covariates
set.seed(2021L)
model1 <- conText(formula = immigration ~ party + gender,
data = toks_nostop_feats,
pre_trained = cr_glove_subset,
transform = TRUE, transform_matrix = cr_transform,
bootstrap = TRUE, num_bootstraps = 100,
permute = TRUE, num_permutations = 100,
window = 6, case_insensitive = TRUE,
verbose = FALSE)## coefficient normed.estimate std.error lower.ci upper.ci p.value
## 1 party_R 3.386745 0.2279633 2.996087 3.734473 0
## 2 gender_M 2.956320 0.2333038 2.604478 3.323050 0# notice, non-binary covariates are automatically "dummified"
rownames(model1)## [1] "party_R" "gender_M" "(Intercept)"conText() outputs a conText-class object
which is simply a dgCMatrix class matrix corresponding to
the beta coefficients (ALC embeddings) with additional attributes
including: a table (automatically printed) with the normed coefficients
(excluding the intercept), their std. errors and p-values,
@normed_coefficients, and the set of features used when
creating the embeddings @features.
We can combine the coefficients to compute the ALC embeddings for the various combinations of the covariates (see Rodriguez, Spirling and Stewart (2021) for details).
# D-dimensional beta coefficients
# the intercept in this case is the ALC embedding for female Democrats
# beta coefficients can be combined to get each group's ALC embedding
DF_wv <- model1['(Intercept)',] # (D)emocrat - (F)emale
DM_wv <- model1['(Intercept)',] + model1['gender_M',] # (D)emocrat - (M)ale
RF_wv <- model1['(Intercept)',] + model1['party_R',] # (R)epublican - (F)emale
RM_wv <- model1['(Intercept)',] + model1['party_R',] + model1['gender_M',] # (R)epublican - (M)ale
# nearest neighbors
nns(rbind(DF_wv,DM_wv), N = 10, pre_trained = cr_glove_subset, candidates = model1@features)## $DM_wv
## # A tibble: 10 × 4
## target feature rank value
## <chr> <chr> <int> <dbl>
## 1 DM_wv immigration 1 0.837
## 2 DM_wv broken 2 0.698
## 3 DM_wv reform 3 0.670
## 4 DM_wv comprehensive 4 0.659
## 5 DM_wv immigrants 5 0.590
## 6 DM_wv illegal 6 0.582
## 7 DM_wv enforcement 7 0.562
## 8 DM_wv system 8 0.550
## 9 DM_wv border 9 0.550
## 10 DM_wv fix 10 0.547
##
## $DF_wv
## # A tibble: 10 × 4
## target feature rank value
## <chr> <chr> <int> <dbl>
## 1 DF_wv immigration 1 0.831
## 2 DF_wv comprehensive 2 0.637
## 3 DF_wv immigrants 3 0.635
## 4 DF_wv enforcement 4 0.627
## 5 DF_wv broken 5 0.623
## 6 DF_wv reform 6 0.608
## 7 DF_wv law 7 0.596
## 8 DF_wv illegal 8 0.589
## 9 DF_wv laws 9 0.569
## 10 DF_wv status 10 0.568To access the normed coefficients for plotting:
model1@normed_coefficients## coefficient normed.estimate std.error lower.ci upper.ci p.value
## 1 party_R 3.386745 0.2279633 2.996087 3.734473 0
## 2 gender_M 2.956320 0.2333038 2.604478 3.323050 0conText can also take continuous covariates. In the
example below we estimate a model using the first dimension of the
NOMINATE score–understood to capture the Liberal-Conservative spectrum
on economic matters. To explore similarity with specific features we use
the fitted model to compute the fitted values –ALC embeddings– at
various percentiles of the NOMINATE score. Consistent with our results
above, the higher the NOMINATE score (more Conservative) the greater the
similarity between the ALC embedding for immigration and
the feature enforcement whereas the lower the NOMINATE
score (more Liberal) the greater its similarity with the feature
reform.
# continuous covariate
set.seed(2021L)
model2 <- conText(formula = immigration ~ nominate_dim1,
data = toks_nostop_feats,
pre_trained = cr_glove_subset,
transform = TRUE, transform_matrix = cr_transform,
bootstrap = TRUE, num_bootstraps = 100,
permute = TRUE, num_permutations = 100,
window = 6, case_insensitive = TRUE,
verbose = FALSE)## coefficient normed.estimate std.error lower.ci upper.ci p.value
## 1 nominate_dim1 3.738801 0.2343684 3.319705 4.092368 0# look at percentiles of nominate
percentiles <- quantile(docvars(cr_sample_corpus)$nominate_dim1, probs = seq(0.05,0.95,0.05))
percentile_wvs <- lapply(percentiles, function(i) model2["(Intercept)",] + i*model2["nominate_dim1",]) %>% do.call(rbind,.)
percentile_sim <- cos_sim(x = percentile_wvs, pre_trained = cr_glove_subset, features = c("reform", "enforce"), as_list = TRUE)
# check output
rbind(head(percentile_sim[["reform"]], 5),tail(percentile_sim[["reform"]], 5))## target feature value
## 1 5% reform 0.6685867
## 2 10% reform 0.6637985
## 3 15% reform 0.6596817
## 4 20% reform 0.6571185
## 5 25% reform 0.6538607
## 15 75% reform 0.5015567
## 16 80% reform 0.4873665
## 17 85% reform 0.4803103
## 18 90% reform 0.4746953
## 19 95% reform 0.4693446rbind(head(percentile_sim[["enforce"]], 5),tail(percentile_sim[["enforce"]], 5))## target feature value
## 20 5% enforce 0.4588883
## 21 10% enforce 0.4729510
## 22 15% enforce 0.4835118
## 23 20% enforce 0.4895358
## 24 25% enforce 0.4966872
## 34 75% enforce 0.6337590
## 35 80% enforce 0.6386407
## 36 85% enforce 0.6408066
## 37 90% enforce 0.6424128
## 38 95% enforce 0.6438501If (a) you have a large enough corpus to train a full GloVe embeddings model and (b) your corpus is distinctive in some way –e.g. a collection of articles from scientific journals–, then you may want to consider estimating your own set of embeddings and transformation matrix –otherwise you should be good to go using GloVe pre-trained embeddings. The first step is to estimate GloVe embeddings on the full corpus which you will then use as your pre-trained embeddings.
This example is taken (with minor changes) from this
quanteda vignette on computing GloVe embeddings using
quanteda and text2vec.
library(text2vec)
#---------------------------------
# estimate glove model
#---------------------------------
# construct the feature co-occurrence matrix for our toks_nostop_feats object (see above)
toks_fcm <- fcm(toks_nostop_feats, context = "window", window = 6, count = "frequency", tri = FALSE) # important to set tri = FALSE
# estimate glove model using text2vec
glove <- GlobalVectors$new(rank = 300,
x_max = 10,
learning_rate = 0.05)
wv_main <- glove$fit_transform(toks_fcm, n_iter = 10,
convergence_tol = 1e-3,
n_threads = 2) # set to 'parallel::detectCores()' to use all available cores## INFO [12:50:17.585] epoch 1, loss 0.2268
## INFO [12:50:18.547] epoch 2, loss 0.0768
## INFO [12:50:19.495] epoch 3, loss 0.0497
## INFO [12:50:20.463] epoch 4, loss 0.0375
## INFO [12:50:21.444] epoch 5, loss 0.0303
## INFO [12:50:22.386] epoch 6, loss 0.0256
## INFO [12:50:23.361] epoch 7, loss 0.0223
## INFO [12:50:24.369] epoch 8, loss 0.0198
## INFO [12:50:25.387] epoch 9, loss 0.0178
## INFO [12:50:26.321] epoch 10, loss 0.0163wv_context <- glove$components
local_glove <- wv_main + t(wv_context) # word vectors
# qualitative check
find_nns(local_glove['immigration',], pre_trained = local_glove, N = 5, candidates = feats)## [1] "immigration" "president" "bill" "law" "congress"Given a corpus and it’s corresponding GloVe embeddings, we can
compute a corresponding transformation matrix using
conText::compute_transform().
# compute transform
# weighting = 'log' works well for smaller corpora
# for large corpora use a numeric value e.g. weighting = 500
# see: https://arxiv.org/pdf/1805.05388.pdf
local_transform <- compute_transform(x = toks_fcm, pre_trained = local_glove, weighting = 'log')You should always run some sanity checks to make sure the computed transformation matrix produces sensical results (keep in mind the corpus in this example consists of only 200 documents). A more robust approach (not illustrated below) to verify your transformation matrix is to randomly select a set of features to exclude from the its estimation but which have a corresponding pre-trained embedding. Use the transformation matrix and pre-trained embeddings to compute ALC embeddings for these excluded features and check their similarity with their corresponding pre-trained embeddings (it should be reasonably high, > 0.6).
#---------------------------------
# check
#---------------------------------
# create document-embedding matrix using our locally trained GloVe embeddings and transformation matrix
immig_dem_local <- dem(x = immig_dfm, pre_trained = local_glove, transform = TRUE, transform_matrix = local_transform, verbose = TRUE)
# take the column average to get a single "corpus-wide" embedding
immig_wv_local <- colMeans(immig_dem_local)
# find nearest neighbors for overall immigraiton embedding
find_nns(immig_wv_local, pre_trained = local_glove, N = 10, candidates = immig_dem_local@features)## [1] "immigration" "bill" "illegal" "country" "states"
## [6] "law" "president" "united" "reform" "people"# we can also compare to corresponding pre-trained embedding
sim2(x = matrix(immig_wv_local, nrow = 1), y = matrix(local_glove['immigration',], nrow = 1), method = 'cosine', norm = 'l2')## [,1]
## [1,] 0.7926645We may be interested in simultaneously embedding many features. This
can be useful to (1) compare two corpora along many features
simultaneously and/or (2) estimate context-specific feature embeddings
that can subsequently be fed into a downstream classification task.
We’ll focus on (1) here. Using the same tokenized
cr_sample_corpus we start by building a
feature-co-occurrence matrix for each group (party).
# create feature co-occurrence matrix for each party (set tri = FALSE to work with fem)
fcm_D <- fcm(toks_nostop_feats[docvars(toks_nostop_feats, 'party') == "D",], context = "window", window = 6, count = "frequency", tri = FALSE)
fcm_R <- fcm(toks_nostop_feats[docvars(toks_nostop_feats, 'party') == "R",], context = "window", window = 6, count = "frequency", tri = FALSE)Given an fcm for each group, we can proceed to compute a
corresponding “feature-embedding-matrix” for each group.
# compute feature-embedding matrix
fem_D <- fem(fcm_D, pre_trained = cr_glove_subset, transform = TRUE, transform_matrix = cr_transform, verbose = FALSE)
fem_R <- fem(fcm_R, pre_trained = cr_glove_subset, transform = TRUE, transform_matrix = cr_transform, verbose = FALSE)
# cr_fem will contain an embedding for each feature
fem_D[1:5,1:3]## 5 x 3 sparse Matrix of class "dgCMatrix"
##
## president 0.3744276 0.2664312 0.5106154
## rise 0.6826160 0.1579899 0.1132372
## today 0.5012829 0.1986229 0.1827530
## honor 0.6683875 0.3323882 0.2093607
## one 0.5223592 0.4007449 0.2207826Finally, we can use the conText::feature_sim function to
compute “horizontal” cosine similarities between both fems
for the set of overlapping features. The output of
feature_sim is ranked from least similar to most similar
features.
# compute "horizontal" cosine similarity
feat_comp <- feature_sim(x = fem_R, y = fem_D)
# least similar features
head(feat_comp)## feature value
## 1 link -0.2160803
## 2 guantanamo -0.1859814
## 3 frustration -0.1853331
## 4 opposite -0.1807265
## 5 draw -0.1754296
## 6 refuses -0.1743808# most similar features
tail(feat_comp)## feature value
## 3501 customs 0.9514645
## 3502 border 0.9520096
## 3503 department 0.9529625
## 3504 homeland 0.9562521
## 3505 enforcement 0.9570831
## 3506 security 0.9584692In all the examples above we’ve focused on contexts defined as
“windows” around a target word. In Rodriguez
et al. (2021) we show that the same procedure works well on contexts
defined as the full document, as long as the full document is relatively
short –exactly what threshold to use to define “short” is still an open
question. Responses to open-ended survey questions is a good example
(discussed in the paper). Below we provide an illustrative example using
a subset of the cr_sample_corpus. In particular, note the
use of the . ~ operator to indicate that the full document
should be used as the DV.
# identify documents with fewer than 100 words
short_toks <- toks_nostop_feats[sapply(toks_nostop_feats, length) <= 100,]
# run regression on full documents
model3 <- conText(formula = . ~ party,
data = short_toks,
pre_trained = cr_glove_subset,
transform = TRUE, transform_matrix = cr_transform,
bootstrap = TRUE, num_bootstraps = 100,
permute = TRUE, num_permutations = 100,
window = 6, case_insensitive = TRUE,
verbose = FALSE)## coefficient normed.estimate std.error lower.ci upper.ci p.value
## 1 party_R 3.878352 0.3682709 3.29526 4.493428 0