You plan to train your model by normalizing your utterances that you receive from your end users.
This will help reduce the variance in extracting intents and entities for your LUIS application.
Which API setting would you enable in your LUIS application to normalize text from Toma una taza de café to Toma una taza de cafe?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B.
Option A is incorrect because you will use UseAllTrainingData if you would want all data as negative sample to be used for non deterministic training.
Option B is correct because NormalizeDiacritics is used to replace normalizing diacritics from the utterance that would replace characters with diacritics.
In this case é is converted to e.
Option C is incorrect because NormalizePunctuation would remove punctuation from the user utterance.
Option D is incorrect because NormalizeWordForm would normalize word forms.
Reference:
To learn more about normalizing user utterances, use the link given below:
The correct answer to the question is option D, NormalizeWordForm.
When creating a LUIS (Language Understanding Intelligent Service) application, it is important to preprocess the text data before training the model. Preprocessing refers to the techniques applied to text data to transform it into a format that the machine learning algorithms can use for training. Normalization is a common preprocessing technique used in NLP (Natural Language Processing) to transform text into a standard form that can help improve the accuracy of the model.
Normalization involves transforming text to a standard form by removing or replacing specific characters and words. In the context of LUIS, normalization can help reduce the variance in extracting intents and entities from user utterances, which in turn can improve the overall performance of the model.
The API setting that would enable text normalization in LUIS is the NormalizeWordForm setting. This setting normalizes the text by removing diacritics (accents and other marks) and punctuation marks, and by converting characters to their base form. For example, in the given sentence "Toma una taza de café", the word "café" would be normalized to "cafe" by removing the accent mark.
Option A, UseAllTrainingData, is a setting that controls whether to include all training data in the model or only the most relevant data. Option B, NormalizeDiacritics, is a setting that only removes diacritics (accents and other marks) from text. Option C, NormalizePunctuation, is a setting that only removes punctuation marks from text. Therefore, options B and C are not sufficient to normalize text fully and may not address all the issues related to text normalization in LUIS.