Alter NLU Engine

Installation

Go to this GitHub Repo and follow the steps in the “Setting Up” section of the README.MD file.

Rest API

Rest API supported for both the below training and parsing queries.

  • REST API training

    URL     : http://<ip_address>:5001/train
Method  : POST
Headers : {
    Accept : application/json,
    Content-Type : application/json
}
Body    : Content of Training Data JSON file downloaded from Alter NLU Console

    or

    curl -H "Content-Type: application/json" --data @<training_data_json_file_path> http://<ip_address>:5001/train

  • Rest API parse query

    URL    : http://<ip_address>:5001/parse
Method : POST
Headers: {
    Accept : application/json,
    Content-Type : application/json
}
Body   : {"text": "<user_query>"}

v1.0.0-beta: The Engineering Involved

Intent Model

We have used Convolutional Neural Networks (CNN) based model to capture the intent. Further, the use of custom validation algorithm and matthews correlation coefficient as accuracy metrics makes the intent model robust.

Entity Model

In this version, we have replaced the previous Flashtext and FuzzyWuzzy based entity extraction method with a CRF based Entity Recognition model.

Alter NLU Engine Response Format

A detailed explanation of the response and an example is given below:

https://s3-ap-southeast-1.amazonaws.com/kontikilabs.com/alter-nlu-readthedocs/example-two.png

  • According to the context of the user query, the model successfully recognises the search product intent along with the confidence score.

  • This model handles out-of-vocabulary words to some extent. The term ‘out-of-vocabulary words’ refers to words that are not present in the training data of the chatbot. For instance, you trained in ALter NLU for the sentence:

    I want to purchase apple mobile worth 60k

    Now, take a look at the input JSON, formed based on the user query in the image above. Even though the parsed_value, “1049k” may not be present in your training dataset, the output recognises the entity accurately as “price”.

  • The CRF model helps is recognising the entity accurately, because it considers the sentence structure of the user query.

  • The main goal of the “parsed_value” key in the response is to assist developers to directly use the key where needed. In the example above, the developer might need the exact value of entities such as “price”, which is in the user query for further usage. In this case it is “1049k”.

  • Also, if you are an existing user of Alter NLU, it needs to be pointed out that the “category” key in the response has been renamed to “name”.