Named Entity Recognition in Texts using 'NameTag'

Wraps the 'nametag' library < https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) < https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.

Named Entity Recognition with the Nametag Maximum Entropy Markov model

This repository contains an R package which wraps the NameTag C++ library (https://github.com/ufal/nametag), allowing the following:

• Fit a Maximum Entropy Markov model machine learning model
• 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.

Example

• Downloading a pretrained named entity recognition model.
• Note: look to the help of function ?nametagger which allows you to train your own named entity recognition model on your own data

library(nametagger)
model <- nametagger_download_model("english-conll-140408", model_dir = tempdir())
x     <- data.frame(doc_id      = c(1, 1, 2),
                    sentence_id = c(1, 2, 1),
                    text        = c("I\nlive\nin\nNew\nYork\nand\nI\nwork\nfor\nApple\nInc.", 
                                    "Why\ndon't\nyou\ncome\nvisit\nme", 
                                    "Good\nnews\nfrom\nAmazon\nas\nJohn\nworks\nthere\n."))
predict(model, x)                          

 doc_id sentence_id term_id   term entity
      1           1       1      I      O
      1           1       2   live      O
      1           1       3     in      O
      1           1       4    New  B-LOC
      1           1       5   York  I-LOC
      1           1       6    and      O
      1           1       7      I      O
      1           1       8   work      O
      1           1       9    for      O
      1           1      10  Apple  B-ORG
      1           1      11   Inc.  I-ORG
      1           2       1    Why      O
      1           2       2  don't      O
      1           2       3    you      O
      1           2       4   come      O
      1           2       5  visit      O
      1           2       6     me      O
      2           1       1   Good      O
      2           1       2   news      O
      2           1       3   from      O
      2           1       4 Amazon  B-LOC
      2           1       5     as      O
      2           1       6   John  B-PER
      2           1       7  works      O
      2           1       8  there      O
      2           1       9      .      O

Installation

• For regular users, install the package from your local CRAN mirror install.packages("nametagger")
• For installing the development version of this package: remotes::install_github("bnosac/nametagger")

Look to the documentation of the functions.

help(package = "nametagger")

Support in text mining

Need support in text mining? Contact BNOSAC: http://www.bnosac.be