vhandit

Check out my implementation here Background I wanted to have a tool for human beings to classify intents and extract entities of texts which were obtained from a raw dataset such as Rocket.chat's conversation, Maluuba Frames or  here . Then, the output (labeled texts) could be consumed by an NLU tool such as Rasa NLU. rasa-nlu-trainer was a potential one which I didn't need to build an app from scratch. However, I needed to add more of my own features to fulfill my needs. They were: 1. Loading/displaying raw texts stored by a database such as MongoDB 2. Manually labeling intents and entities for the loaded texts 3. Persisting labeled texts into the database I firstly did look up what rasa-nlu-trainer 's technologies were used in order to see how to implement my mentioned features. At first glance rasa-nlu-trainer was bootstrapped with Create React App. Create React App is a tool to create a React app with no build configuration, as it said. This too

According to this nice article , there was a new pipeline released using a different approach from the standard one ( spacy_sklearn ). I wanted to give it a try to see whether it can help with improving bot’s accuracy. After applying done, I gave an evaluation of “tensorflow_embedding”. It seemed to work better a bit. For example, I defined intents “greet” and “goodbye” with some following messages in my training data. ## intent:greet - Hey! How are you? - Hi! How can I help you? - Good to see you! - Nice to see you! - Hi - Hello - Hi there ## intent:goodbye - Bye - Bye Bye - See you later - Take care - Peace In order to play around with Rasa NLU, I created a project here . You can have a look at this change from this pull request . Yay! When I entered message “hi bot”, then bot with “tensorflow_embedding” could detect intent “greet” with better confidence scores rather than bot with “spacy_sklearn”. The following are responses after executing curl -X POST loc