metadata

license: cc-by-4.0

The Tokenizer for Clinical Cases Written in Spanish

Introduction

This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish.

This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017.

This repository contains the training set, testing set, Gold Standard.

Prerequisites

This software has been compiled with Java SE 1.8 and it should work with recent versions. You can download Java from the following website: https://www.java.com/en/download

The executable file already includes the Apache OpenNLP dependencies inside, so the download of this toolkit is not necessary. However, you may download the latest version from this website: https://opennlp.apache.org/download.html

The library file we have used to compile is "opennlp-tools-1.8.4.jar". The source code should be able to compile with the latest version of OpenNLP, "opennlp-tools-RELEASE_NUMBER.jar". In case there are compilation or execution errors, please let us know and we will make all the necessary updates.

Directory structure

exec/
  An executable file that can be used to apply the tokenization to your documents.
  You can find the notes about its execution below in section "Usage".

gold_standard/
  The clinical cases used as gold standard to evaluate the model's performance.

model/
  The tokenizationint model, "es-tokenization-model-spaccc.bin", a binary file.

src/
  The source code to create the model (CreateModelTok.java) and evaluate it (EvaluateModelTok.java).
  The directory includes an example about how to use the model inside your code (Tokenization.java).
  File "abbreviations.dat" contains a list of abbreviations, essential to build the model.

test_set/
  The clinical cases used as test set to evaluate the model's performance.

train_set/
  The clinical cases used to build the model. We use a single file with all documents present in
  directory "train_set_docs" concatented.

train_set_docs/
  The clinical cases used to build the model. For each record the sentences are already splitted.

Usage

The executable file Tokenizer.jar is the program you need to tokenize the text in your document. For this program, two arguments are needed: (1) the text file to tokenize, and (2) the model file (es-tokenization-model-spaccc.bin). The program will display all tokens in the terminal, with one token per line.

From the exec folder, type the following command in your terminal:

$ java -jar Tokenizer.jar INPUT_FILE MODEL_FILE

Examples

Assuming you have the executable file, the input file and the model file in the same directory:

$ java -jar Tokenizer.jar file.txt es-tokenizer-model-spaccc.bin

Model creation

To create this tokenization model, we used the following training parameters (class TrainingParameters in OpenNLP) to get the best performance:

Number of iterations: 1500.
Cutoff parameter: 4.
Trainer type parameter: EventTrainer.EVENT_VALUE.
Algorithm: Maximum Entropy (ModelType.MAXENT.name()).

Meanwhile, we used the following parameters for the tokenizer builder (class TokenizerFactory in OpenNLP) to get the best performance:

Language code: es (for Spanish).
Abbreviation dictionary: file "abbreviations.dat" (included in the src/ directory).
Use alphanumeric optimization: false
Alphanumeric pattern: null

Model evaluation

After tuning the model using different values for each parameter mentioned above, we got the best performance with the values mentioned above.

	Value
Number of tokens in the gold standard	38247
Number of tokens generated	38227
Number of words correctly tokenized	38182
Number of words wrongly tokenized	35
Number of tokens missed	30
Precision	99.88%
Recall	99.83%
F-Measure	99.85%

Table 1: Evaluation statistics for the tokenization model.

Contact

Ander Intxaurrondo ([email protected])

License

This work is licensed under a Creative Commons Attribution 4.0 International License.