parLapply and Part of Speech tagging - r

Question

I am trying to use the parLapply along with the openNLP R package to do part of speech tagging of a corpus of ~600k documents. However, while I was able to successfully part of speech tag a different set of ~90k documents, I get a strange error after ~25 mins of running the same code over the ~600k documents:
Error in checkForRemoteErrors(val) : 10 nodes produced errors; first error: no word token annotations found
The documents are simply digital newspaper articles, where I run the tagger over the body field (after cleaning). This field is nothing but raw text which I save into a list of strings.
Here's my code:
# I set the Java heap size (memory) allocation - I experimented with different sizes
options(java.parameters = "- Xmx3GB")
# Convert the corpus into a list of strings
myCorpus <- lapply(contentCleaned, function(x){x <- as.String(x)})
# tag Corpus Function
tagCorpus <- function(x, ...){
s <- as.String(x) # This is a repeat and may not be required
WTA <- Maxent_Word_Token_Annotator()
a2 <- Annotation(1L, "sentence", 1L, nchar(s))
a2 <- annotate(s, WTA, a2)
a3 <- annotate(s, PTA, a2)
word_subset <- a3[a3$type == "word"]
POStags <- unlist(lapply(word_subset$features, `[[`, "POS"))
POStagged <- paste(sprintf("%s/%s", s[word_subset], POStags), collapse = " ")
list(text = s, POStagged = POStagged, POStags = POStags, words = s[word_subset])
}
# I have 12 cores in my box
cl <- makeCluster(mc <- getOption("cl.cores", detectCores()-2))
# I tried both exporting the word token annotator and not
clusterEvalQ(cl, {
library(openNLP);
library(NLP);
PTA <- Maxent_POS_Tag_Annotator();
WTA <- Maxent_Word_Token_Annotator()
})
# Each cluster node has the following description:
[[1]]
An annotator inheriting from classes
Simple_Word_Token_Annotator Annotator
with description
Computes word token annotations using the Apache OpenNLP Maxent tokenizer employing the default model for language 'en'.
clusterEvalQ(cl, sessionInfo())
# ClusterEvalQ outputs for each worker:
[[1]]
R version 3.4.4 (2018-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
[9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8 LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] NLP_0.1-11 openNLP_0.2-6
loaded via a namespace (and not attached):
[1] openNLPdata_1.5.3-4 compiler_3.4.4 parallel_3.4.4 rJava_0.9-10
packageDescription('openNLP') # Version: 0.2-6
packageDescription('parallel') # Version: 3.4.4
startTime <- Sys.time()
print(startTime)
corpus.tagged <- parLapply(cl, myCorpus, tagCorpus)
endTime <- Sys.time()
print(endTime)
endTime - startTime
Kindly note that I have consulted many web forums & the one which stood out is:
parallel parLapply setup
However, this doesn't seem to address my issue. Furthermore, I am confused why the setup works with the ~90k articles but not the ~600k articles (I have a total of 12 cores and 64GB memory). Any advice is much appreciated.

Answer 1

I have managed to get this to work by directly using the qdap package (https://github.com/trinker/qdap) by Tyler Rinker. It took ~20 hours to run. Here's how the function pos from the qdap package does this in a one liner:
corpus.tagged <- qdap::pos(myCorpus, parallel =TRUE, cores =detectCores()-2)