public static async Task PatternSpotter(string testString)
{
Storage.Current = new OnlineRepositoryStorage(new DiskStorage("catalyst-models"));
//Console.WriteLine("Loading models... This might take a bit longer the first time you run this sample, as the models have to be downloaded from the online repository");
var nlp = await Pipeline.ForAsync(Language.English);
nlp.Add(await AveragePerceptronEntityRecognizer.FromStoreAsync(language: Language.English, version: Version.Latest, tag: "WikiNER"));
var isApattern = new PatternSpotter(Language.English, 0, tag: "is-a-pattern", captureTag: "IsA");
isApattern.NewPattern(
"Is+Noun",
mp => mp.Add(
new PatternUnit(P.Single().WithToken("is").WithPOS(PartOfSpeech.VERB)),
new PatternUnit(P.Multiple().WithPOS(PartOfSpeech.NOUN, PartOfSpeech.PROPN, PartOfSpeech.AUX, PartOfSpeech.DET, PartOfSpeech.ADJ))
));
nlp.Add(isApattern);
var doc = new Document(testString, Language.English);
nlp.ProcessSingle(doc);
PrintDocumentEntities(doc);
}
private static async Task DemonstrateAveragePerceptronEntityRecognizerAndPatternSpotter()
{
// For training an AveragePerceptronModel, check the source-code here: https://github.com/curiosity-ai/catalyst/blob/master/Catalyst.Training/src/TrainWikiNER.cs
// This example uses the pre-trained WikiNER model, trained on the data provided by the paper "Learning multilingual named entity recognition from Wikipedia", Artificial Intelligence 194 (DOI: 10.1016/j.artint.2012.03.006)
// The training data was sourced from the following repository: https://github.com/dice-group/FOX/tree/master/input/Wikiner
//Configures the model storage to use the online repository backed by the local folder ./catalyst-models/
Storage.Current = new OnlineRepositoryStorage(new DiskStorage("catalyst-models"));
//Create a new pipeline for the english language, and add the WikiNER model to it
Console.WriteLine("Loading models... This might take a bit longer the first time you run this sample, as the models have to be downloaded from the online repository");
var nlp = await Pipeline.ForAsync(Language.English);
nlp.Add(await AveragePerceptronEntityRecognizer.FromStoreAsync(language: Language.English, version: Version.Latest, tag: "WikiNER"));
//Another available model for NER is the PatternSpotter, which is the conceptual equivalent of a RegEx on raw text, but operating on the tokenized form off the text.
//Adds a custom pattern spotter for the pattern: single("is" / VERB) + multiple(NOUN/AUX/PROPN/AUX/DET/ADJ)
var isApattern = new PatternSpotter(Language.English, 0, tag: "is-a-pattern", captureTag: "IsA");
isApattern.NewPattern(
"Is+Noun",
mp => mp.Add(
new PatternUnit(P.Single().WithToken("is").WithPOS(PartOfSpeech.VERB)),
new PatternUnit(P.Multiple().WithPOS(PartOfSpeech.NOUN, PartOfSpeech.PROPN, PartOfSpeech.AUX, PartOfSpeech.DET, PartOfSpeech.ADJ))
));
nlp.Add(isApattern);
//For processing a single document, you can call nlp.ProcessSingle
var doc = new Document(Data.Sample_1, Language.English);
nlp.ProcessSingle(doc);
//For processing a multiple documents in parallel (i.e. multithreading), you can call nlp.Process on an IEnumerable<IDocument> enumerable
var docs = nlp.Process(MultipleDocuments());
//This will print all recognized entities. You can also see how the WikiNER model makes a mistake on recognizing Amazon as a location on Data.Sample_1
PrintDocumentEntities(doc);
foreach (var d in docs)
{
PrintDocumentEntities(d);
}
//For correcting Entity Recognition mistakes, you can use the Neuralyzer class.
//This class uses the Pattern Matching entity recognition class to perform "forget-entity" and "add-entity"
//passes on the document, after it has been processed by all other proceses in the NLP pipeline
var neuralizer = new Neuralyzer(Language.English, 0, "WikiNER-sample-fixes");
//Teach the Neuralyzer class to forget the match for a single token "Amazon" with entity type "Location"
neuralizer.TeachForgetPattern("Location", "Amazon", mp => mp.Add(new PatternUnit(P.Single().WithToken("Amazon").WithEntityType("Location"))));
//Teach the Neuralyzer class to add the entity type Organization for a match for the single token "Amazon"
neuralizer.TeachAddPattern("Organization", "Amazon", mp => mp.Add(new PatternUnit(P.Single().WithToken("Amazon"))));
//Add the Neuralyzer to the pipeline
nlp.UseNeuralyzer(neuralizer);
//Now you can see that "Amazon" is correctly recognized as the entity type "Organization"
var doc2 = new Document(Data.Sample_1, Language.English);
nlp.ProcessSingle(doc2);
PrintDocumentEntities(doc2);
}
public static async Task Main(string[] args)
{
Console.OutputEncoding = Encoding.UTF8;
ApplicationLogging.SetLoggerFactory(LoggerFactory.Create(lb => lb.AddConsole()));
// Catalyst currently supports 3 different types of models for Named Entity Recognition (NER):
// - Gazetteer-like(i.e. [Spotter](https://github.com/curiosity-ai/catalyst/blob/master/Catalyst/src/Models/EntityRecognition/Spotter.cs))
// - Regex-like(i.e. [PatternSpotter](https://github.com/curiosity-ai/catalyst/blob/master/Catalyst/src/Models/EntityRecognition/PatternSpotter.cs))
// - Perceptron (i.e. [AveragePerceptronEntityRecognizer](https://github.com/curiosity-ai/catalyst/blob/master/Catalyst/src/Models/EntityRecognition/AveragePerceptronEntityRecognizer.cs))
// For training an AveragePerceptronModel, check the source-code here: https://github.com/curiosity-ai/catalyst/blob/master/Catalyst.Training/src/TrainWikiNER.cs
// This example uses the pre-trained WikiNER model, trained on the data provided by the paper "Learning multilingual named entity recognition from Wikipedia", Artificial Intelligence 194 (DOI: 10.1016/j.artint.2012.03.006)
// The training data was sourced from the following repository: https://github.com/dice-group/FOX/tree/master/input/Wikiner
//Configures the model storage to use the online repository backed by the local folder ./catalyst-models/
Storage.Current = new OnlineRepositoryStorage(new DiskStorage("catalyst-models"));
//Create a new pipeline for the english language, and add the WikiNER model to it
Console.WriteLine("Loading models... This might take a bit longer the first time you run this sample, as the models have to be downloaded from the online repository");
var nlp = await Pipeline.ForAsync(Language.English);
nlp.Add(await AveragePerceptronEntityRecognizer.FromStoreAsync(language: Language.English, version: Version.Latest, tag: "WikiNER"));
//Another available model for NER is the PatternSpotter, which is the conceptual equivalent of a RegEx on raw text, but operating on the tokenized form off the text.
//Adds a custom pattern spotter for the pattern: single("is" / VERB) + multiple(NOUN/AUX/PROPN/AUX/DET/ADJ)
var isApattern = new PatternSpotter(Language.English, 0, tag: "is-a-pattern", captureTag: "IsA");
isApattern.NewPattern("Is+Noun", mp => mp.Add(new PatternUnit(P.Single().WithToken("is").WithPOS(PartOfSpeech.VERB)),
new PatternUnit(P.Multiple().WithPOS(PartOfSpeech.NOUN, PartOfSpeech.PROPN, PartOfSpeech.AUX, PartOfSpeech.DET, PartOfSpeech.ADJ))
));
nlp.Add(isApattern);
//For processing a single document, you can call nlp.ProcessSingle
var doc = new Document(Data.Sample_1, Language.English);
nlp.ProcessSingle(doc);
//For processing a multiple documents in parallel (i.e. multithreading), you can call nlp.Process on an IEnumerable<IDocument> enumerable
var docs = nlp.Process(MultipleDocuments());
//This will print all recognized entities. You can also see how the WikiNER model makes a mistake on recognizing Amazon as a location on Data.Sample_1
PrintDocumentEntities(doc);
foreach (var d in docs)
{
PrintDocumentEntities(d);
}
//For correcting Entity Recognition mistakes, you can use the Neuralyzer class.
//This class uses the Pattern Matching entity recognition class to perform "forget-entity" and "add-entity"
//passes on the document, after it has been processed by all other proceses in the NLP pipeline
var neuralizer = new Neuralyzer(Language.English, 0, "WikiNER-sample-fixes");
//Teach the Neuralyzer class to forget the match for a single token "Amazon" with entity type "Location"
neuralizer.TeachForgetPattern("Location", "Amazon", mp => mp.Add(new PatternUnit(P.Single().WithToken("Amazon").WithEntityType("Location"))));
//Teach the Neuralyzer class to add the entity type Organization for a match for the single token "Amazon"
neuralizer.TeachAddPattern("Organization", "Amazon", mp => mp.Add(new PatternUnit(P.Single().WithToken("Amazon"))));
//Add the Neuralyzer to the pipeline
nlp.UseNeuralyzer(neuralizer);
//Now you can see that "Amazon" is correctly recognized as the entity type "Organization"
var doc2 = new Document(Data.Sample_1, Language.English);
nlp.ProcessSingle(doc2);
PrintDocumentEntities(doc2);
//Another way to perform entity recognition is to use a gazeteer-like model. For example, here is one for capturing a set of programing languages
var spotter = new Spotter(Language.Any, 0, "programming", "ProgrammingLanguage");
spotter.Data.IgnoreCase = true; //In some cases, it might be better to set it to false, and only add upper/lower-case exceptions as required
spotter.AddEntry("C#");
spotter.AddEntry("Python");
spotter.AddEntry("Python 3"); //entries can have more than one word, and will be automatically tokenized on whitespace
spotter.AddEntry("C++");
spotter.AddEntry("Rust");
spotter.AddEntry("Java");
var nlp2 = Pipeline.TokenizerFor(Language.English);
nlp2.Add(spotter); //When adding a spotter model, the model propagates any exceptions on tokenization to the pipeline's tokenizer
var docAboutProgramming = new Document(Data.SampleProgramming, Language.English);
nlp.ProcessSingle(docAboutProgramming);
PrintDocumentEntities(docAboutProgramming);
}