# 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)

license:cc-by-4.0 region:us
{"license": "cc-by-4.0"}
TAGS
#license-cc-by-4.0 #region-us 

The Tokenizer for Clinical Cases Written in Spanish
===================================================


Introduction
------------


This repository contains the tokenization model trained using the SPACCC\_TOKEN corpus (URL 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 (URL 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: URL


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: URL


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, "URL", a binary file.

src/
  The source code to create the model (URL) and evaluate it (URL).
  The directory includes an example about how to use the model inside your code (URL).
  File "URL" 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 *URL* 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 (*URL*). 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 URL INPUT_FILE MODEL_FILE

```

Examples
--------


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



```

$ java -jar URL URL URL

```

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 (*URL()*).


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 "URL" (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.



Table 1: Evaluation statistics for the tokenization model.


Contact
-------


Ander Intxaurrondo (ander.intxaurrondo@URL)


License
-------


<a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL />  
This work is licensed under a <a rel="license" href="URL Commons Attribution 4.0 International License.


Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)



TAGS
#license-cc-by-4.0 #region-us 
