Intent Classification — Demystifying RasaNLU — Part 4

Internals of a chatbot engine — Intent Classification

The series, Demystifying RasaNLU started with an aim of understanding what happens underneath a chatbot engine. RasaNLU being an open source framework, I could read through the code to understand its internals.

The first two parts explains major functionalities of any bot framework, Training and Deploying the Chatbot. Part 3 was aimed to arrive at a deep understanding of the machine learning aspects of Entity Extraction. This part deep-dives into the intent classification.

Intent classification builds a machine learning model, using a prepossessed training data and classifies the user’s text message to an intended action.

For example, a message like “Help me find a Mexican restaurant in Chennai” should be mapped to an action called “restautant_search” with “Mexican” and “Chennai” as search parameters. In the previous part we have seen how to extract these parameters using Named Entity Recognition.

RasaNLU and Intent Classifiers

RasaNLU supports multiple intent classifiers like sklearn, mitie and tensorflow_embedding that can be configured based on your use-case. For the purpose of this blog we are going to stick with sklearn intent classifier.

Training Sample

RasaNLU has a specific data structure for its training data. You can visualize it as a table of sentences each associated with a tag(intent).

hi                                   - greet
hello - greet
bye - end_conversation
goodbye - end_conversation
see you - end_conversation
Suggest me some mexican restaurants - restaurant_search
im looking for restaurants - restaurant_search


For ML models to understand our text data, it needs to be in certain format. The preprocessing step takes care of this transformation. The aim of preprocessing is to arrive at a bunch of features and classes.

  1. Tokenize text to words
"Suggest me some mexican restaurants"
["suggest", "me", "some", "mexican", "restaurants"]

2. Convert words to features

RasaNLU uses spacy word2vec to convert words to numbers. During this process the tokens are mapped in a N-Dimensional space based on its similarity with other words in spacy’s pre-trained corpus.

["suggest", "me", "some", "mexican", "restaurants"]
[1.40101, 1.3003, 0.45647, 1.8934, 1.67677]

3. Labeling Features

Intents are categorized by a label given in training data. These labels are usually the actions the intent meant to perform. Few examples of intent labels are book_tickets, cancel_tickets etc.,

We have converted text-samples from words to numbers for our ML model to understand it. When it comes to labels we don’t convert it into word vectors rather assign a unique number to it. This process is called as encoding.

RasaNLU uses Sklearn's LabelEncoder to perform this step

>>> labels
[greet, good_bye, restuarant_search, good_bye]
>>> le = LabelEncoder() 
>>> y = le.fit_transform(labels)
>>> y
[0, 1, 2, 1 ...

Classify Intents

The ML algorithm that RasaNLU uses to classify intents is the SVM with GridSearchCV. The advantage of GridSearchCV is that you can train SVM with different configurations and at the end of training it returns a trained SVM model with the best configuration.


The configuration defines list of all possible parameter GridSearchCV would train SVM on. GridSearchCV will create SVM’s with all the combinations.

defaults = {                              
"C": [1, 2, 5, 10, 20, 100],
"kernels": ["linear"], "max_cross_validation_folds": 5} C = defaults["C"]
kernels = defaults["kernels"]
tuned_parameters = [{"C": C, "kernel": [str(k) for k in kernels]}]                                                                 
folds = defaults["max_cross_validation_folds"] cv_splits = max(2, min(folds, np.min(np.bincount(y)) // 5))


  1. Create GridSearchCV object with defined configuration
  2. Fit the training data using fit method.

After fitting the data, we will have an N-Dimensional statistical model which can classify similar texts to one of the trained intents.

clf = GridSearchCV( 
SVC(C=1, probability=True, class_weight='balanced'),
param_grid=tuned_parameters, n_jobs=1, cv=cv_splits, scoring='f1_weighted', verbose=1), y)


First let’s test our ML model with one training example from our training data. Since we already have the features extracted lets feed it into clf.predict method. The prediction returns an numerical value which needs to be decoded to arrive at the actual intent tag.

# show me few mexican restaurants
>>> X_test = intent_examples[24].get("text_features").reshape(1, -1)
>>> pred_result = clf.predict(X_test)
>>> pred_result
>>> le.inverse_transform(pred_result)

Apart from testing it over the training data you also need to test it over a bunch of testing data, data that the ML model have never seen before to avoid over fitting.


One major thing that we need to keep in mind while deploying the ML model is that the preprocessing pipeline should remain the same during training, testing and when it is in action. Any change in the pipeline parameters will have a huge impact on the end result.

Now how does RasaNLU keeps these configurations intact? Checkout Part 2 where we cover more about meta-data configuration.

With this we have came to end of the series of Demystifying RasaNLU and learning its internals. Hope you have enjoyed and learned a lot from this series. Do share your questions and comments below.

Intent Classification — Demystifying RasaNLU — Part 4 was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

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