UralicNLP 1.0.0

UralicNLP.net

Download models

Before using the library, you will need to download the models for each language.

var nlp = new UralicApi();
nlp.Download("fin");

Just pass the 3-letter ISO code to the Download function. You can find a list of supported languages in the model catalog

Morphological analysis

You can analyze words' morphology like this

var nlp = new UralicApi();
var analyses = nlp.Analyze("voita", "fin");
foreach (var analysis in analyses){
    Console.WriteLine(analysis.ToString());
}

Analyze takes in a word form and a language code. It returns a list of Result objects. The output of the previous code is:

vuo+N+Pl+Par
voi+N+Sg+Par
voi+N+Pl+Par
voittaa+V+Act+Ind+Prs+ConNeg
voittaa+V+Act+Imprt+Prs+ConNeg+Sg2
voittaa+V+Act+Imprt+Sg2
voitaa+V+Act+Ind+Prs+ConNeg
voitaa+V+Act+Imprt+Prs+ConNeg+Sg2
voitaa+V+Act+Imprt+Sg2

Lemmatization

Sometimes you only want to lemmatize words.

var nlp = new UralicApi();
var analyses = nlp.Lemmatize("voita", "fin");
foreach (var analysis in analyses){
    Console.WriteLine(analysis);
}

This outputs:

vuo
voi
voittaa
voitaa

For compounds, it is possible to mark the word boundaries:

var nlp = new UralicApi();
var analyses = nlp.Lemmatize("luutapiiri", "fin", true);
foreach (var analysis in analyses){
    Console.WriteLine(analysis);
}

Output:

luu|tapiiri
luuta|piiri

Generation

You can also inflect words given their lemma and morphology

var nlp = new UralicApi();
var analyses = nlp.Generate("koira+N+Sg+Par", "fin");
foreach (var analysis in analyses){
    Console.WriteLine(analysis.ToString());
}

Output:

koiraa

Tokenization

You can tokenize text like this:

var tokenizer = new Tokenizer();
var text = "An example sentence. Another, super cool sentence!";
var tokens = tokenizer.Tokenize(text);
foreach (var sentence in tokens)
{
    foreach (var word in sentence)
    {
        Console.Write(word + " ");
    }
    Console.WriteLine();
}

Output:

An example sentence . 
Another , super cool sentence ! 

If you want to only do sentence or word tokenization, you can use Tokenizer.Sentences("text") and Tokenizer.Words("text") respectively