Labelling Sequential Data in Natural Language Processing
This repository contains an R package which wraps the CRFsuite C/C++ library (https://github.com/chokkan/crfsuite), allowing the following:
- Fit a Conditional Random Field model (1st-order linear-chain Markov)
- Use the model to get predictions alongside the model on new data
- The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind.
For users unfamiliar with Conditional Random Field (CRF) models, you can read this excellent tutorial http://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf
- The package is on CRAN, so just install it with the command
- For installing the development version of this package:
devtools::install_github("bnosac/crfsuite", build_vignettes = TRUE)
Model building and tagging
For detailed documentation on how to build your own CRF tagger for doing NER / Chunking. Look to the vignette.
library(crfsuite) vignette("crfsuite-nlp", package = "crfsuite")
library(crfsuite) ## Get example training data + enrich with token and part of speech 2 words before/after each token x <- ner_download_modeldata("conll2002-nl") x <- crf_cbind_attributes(x, terms = c("token", "pos"), by = c("doc_id", "sentence_id"), from = -2, to = 2, ngram_max = 3, sep = "-") ## Split in train/test set crf_train <- subset(x, data == "ned.train") crf_test <- subset(x, data == "testa") ## Build the crf model attributes <- grep("token|pos", colnames(x), value=TRUE) model <- crf(y = crf_train$label, x = crf_train[, attributes], group = crf_train$doc_id, method = "lbfgs", options = list(max_iterations = 25, feature.minfreq = 5, c1 = 0, c2 = 1)) model ## Use the model to score on existing tokenised data scores <- predict(model, newdata = crf_test[, attributes], group = crf_test$doc_id) table(scores$label) B-LOC B-MISC B-ORG B-PER I-LOC I-MISC I-ORG I-PER O 261 211 182 693 24 205 209 605 35297
Build custom CRFsuite models
The package itself does not contain any models to do NER or Chunking. It's a package which facilitates creating your own CRF model for doing Named Entity Recognition or Chunking on your own data with your own categories.
In order to facilitate creating training data on your own data, a shiny app is made available in this R package which allows you to easily tag your own chunks of text, with your own categories. More details can be found in the vignette
vignette("crfsuite-nlp", package = "crfsuite").
Support in text mining
Need support in text mining? Contact BNOSAC: http://www.bnosac.be