| # Technical Documentation |
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| ## Data |
| The projects requires additional runtime data to be able to performs its operations. These are the dictionaries in csv format: |
| - data/lang/es: Spanish dictionaries. |
| - data/lang/gl: Gallego dictionaries. |
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| - data/lang/gl/abr.txt: Contains a series of abbreviations, along with their expanded form. |
| - data/lang/gl/acron.txt: Contains a series of acronyms, along with their expanded form. |
| - data/lang/gl/adxectivos.txt: Contains a series of adjectives, along with their number and gender. |
| - data/lang/gl/bigramas.dat: Binary data of a 2-gram neural network. |
| - data/lang/gl/bigramas_real.dat: Binary data of a 2-gram neural network. |
| - data/lang/gl/clases_ambiguedad.dat: Binary data of a neural network that collapses the grammar category of a word. |
| - data/lang/gl/derivativos.txt: Contain rules for generating derivative words from certain rules. |
| - data/lang/gl/desinencias.txt: Contains a series of verb endings. |
| - data/lang/gl/locucions.txt: Contains a series of verb locucions, along with their grammar category. |
| - data/lang/gl/nomes.txt: Contains a series nouns, with their number and gender. |
| - data/lang/gl/palabrasFunction.txt: Dictionary with all the function words. |
| - data/lang/gl/pentagramas.dat: Binary data of a 5-gram neural network. |
| - data/lang/gl/pentagramas_real.dat: Binary data of a 5-gram neural network. |
| - data/lang/gl/perifrase.txt: Dictionary with all the periphrases. |
| - data/lang/gl/tetragramas.dat: Binary data of a 4-gram neural network. |
| - data/lang/gl/tetragramas_real.dat: Binary data of a 4-gram neural network. |
| - data/lang/gl/trigramas.dat: Binary data of a 3-gram neural network. |
| - data/lang/gl/trigramas.dat: Binary data of a 3-gram neural network. |
| - data/lang/gl/unigramas.dat: Binary data of a 1-gram neural network. |
| - data/lang/gl/utf8chars.csv: Contains a series of custom transformation from UTF-8 words to latin1 |
| - data/lang/gl/verbos.txt: Contains all the verbs, along with their roots. |
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| ## Classes |
| - Cotovia: The primary entry point of the application. It handles the loading and unloading of dictionaries and processes sentences. |
| - PreprocessorUTF8::Processor: Handles the transformation of strings in UTF-8 into latin1. |
| - Tokenizar: Manages the tokenization of an input string |
| - Preproceso: Applies a series of transformations to a tokenized input string to ensure it is in a canonical form. |
| - Analisis_morfologico: Applies a morphologic analysis in which it deduces the classification of each token without contextual information. |
| - Analisis_morfosintactico: Uses a neural network and an n-gram model to compute the classification of each word. |
| - Sintagma: Performs syntagma analysis on the input tokens, clustering the tokens into groups. |
| - Transcripcion: Takes a tokenized sentence and transforms it into multiple phonemes. |
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| ## Tests |
| In the "tests" folder, there is a python script that performs integration tests on the cotovia projects. The following aspects are tested: |
| - Allomorphs of articles in affirmative sentences with a period |
| - Allomorphs of articles in affirmative sentences |
| - Allomorphs of articles in exclamatory sentences |
| - Allomorphs of articles in interrogative sentences |
| - Galician recognition |
| - Galician voice |
| - Feminine names |
| - Masculine names |
| - Number recognition |
| - Number pronunciation |
| - Ordinal numbers |
| - Roman numerals |
| - Punctuation marks |
| - Punctuation marks (variant N) |
| - Phonemes |
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