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---
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
<pre>
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.
</pre>
## 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:
<pre>
$ java -jar Tokenizer.jar INPUT_FILE MODEL_FILE
</pre>
## Examples
Assuming you have the executable file, the input file and the model file in the same directory:
<pre>
$ java -jar Tokenizer.jar file.txt es-tokenizer-model-spaccc.bin
</pre>
## 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 (ander.intxaurrondo@bsc.es)
## License
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)
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