url's changed

R/data.R

@@ -165,7 +165,7 @@ ner_download_modeldata <- function(type = c("conll2002-nl", "conll2002-es", "Ger
 #' }
 #' @name airbnb
 #' @docType data
-#' @source \url{http://data.insideairbnb.com/belgium/bru/brussels/2015-10-03/visualisations/reviews.csv}, \url{http://insideairbnb.com/get-the-data.html}
+#' @source \url{http://insideairbnb.com/brussels}: information of 2015-10-03
 #' @seealso \code{\link{airbnb_chunks}}
 #' @examples
 #' data(airbnb)

README.md

@@ -6,7 +6,7 @@ This repository contains an R package which wraps the CRFsuite C/C++ library (ht
 - 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
+For users unfamiliar with Conditional Random Field (CRF) models, you can read this excellent tutorial https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf
 
 ## Installation
 

vignettes/crfsuite-nlp.Rmd

@@ -32,7 +32,7 @@ This R package wraps the CRFsuite C/C++ library (https://github.com/chokkan/crfs
 - 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
+For users unfamiliar with Conditional Random Field (CRF) models, you can read this excellent tutorial https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf
 
 
 ## Data format