https://github.com/silviomessi/nlu

Python library for Natural Language Understanding
https://github.com/silviomessi/nlu

chatbot natural-language-understanding nlu

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Python library for Natural Language Understanding

• Host: GitHub
• URL: https://github.com/silviomessi/nlu
• Owner: SilvioMessi
• Created: 2017-06-22T09:20:08.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-07-14T10:31:02.000Z (over 7 years ago)
• Last Synced: 2024-04-20T17:23:42.942Z (9 months ago)
• Topics: chatbot, natural-language-understanding, nlu
• Language: Python
• Size: 184 KB
• Stars: 7
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NLU

Python library for NLU (Natural Language Understanding).  

This python library allows to identify intents and entities present in a sentence, starting from a train set of examples. 

## Installation

``` 

pip install git+https://github.com/SilvioMessi/nlu.git 

```

### Dependencies

*  Stanford CoreNLP  

At the moment all the NLP (Natural Language Processing) task are made by [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/), used like external [server](https://stanfordnlp.github.io/CoreNLP/corenlp-server.html).  

Before use NLU library make sure that an instance of CoreNLP Server is correctly running on port 9000.

* Wordnet  

The NLU library use [Wordnet](https://wordnet.princeton.edu/).  

Before use NLU library make sure that Wordnet corpora is correctly "intstalled" by [NTLK](http://www.nltk.org/data.html).

## Basic Usage

```python

from nlu.entity_recognizer import EntityRecognizer

from nlu.intent_recognizer import IntentRecognizer

entity_recongizer = EntityRecognizer()

intent_recognizer = IntentRecognizer()

    

# load entities definitions from datastore and put them in data structure

 entities = {

  'pizza_type': {

    'Margherita' : ['Margherita', 'margherita'],

    'Neapolitan' : ['Neapolitan', 'neapolitan'],

    'Sicilian' : ['Sicilian', 'sicilian']

  },

  'drink_type' : {

    'Coca-Cola' : ['Coca-Cola', 'coca cola', 'coke'],

    'Beer' : ['Beer', 'beer'],

    'Water' : ['Water', 'water'],

    'Wine' : ['Wine', 'wine']

  }

}

# load intents definitions from datastore and put them in data structure

intents = {

  'order_a_pizza': ['Can i have a pizza margherita?', 'A pizza margherita, please !', 'Two margherita please!'],

  'order_a_drink': ['Can i have a can of coke?', 'I\'ll have a glass of wine!']

}

# intents definitions can be pre tagged with entities

intents_pre_tagged = {

  'order_a_pizza': ['Can i have a pizza pizza_type?', 'A pizza pizza_type, please !', 'Two pizza_type please!'],

  'order_a_drink': ['Can i have a can of drink_type?', 'I\'ll have a glass of drink_type!']

}

new_sentence = 'I want order a pizza margherita and a can of coca cola!'

# find entities in new sentence

sentence_tokens, final_entities_positions, tagged_sentence = entity_recongizer.get_entities(entities, new_sentence, tag_sentence=True)

print(sentence_tokens)

['I', 'want', 'order', 'a', 'pizza', 'margherita', 'and', 'a', 'can', 'of', 'coca', 'cola', '!']

print(final_entities_positions)

[{'entity_id': 'pizza_type', 'value_id': 'Margherita', 'start': 21, 'end': 31}, {'entity_id': 'drink_type', 'value_id': 'Coca-Cola', 'start': 45, 'end': 54}]

# if parameter tag_sentence=True

print(tagged_sentence)

I want order a pizza pizza_type and a can of drink_type !

# find intents probabilities 

intents_probabilities = intent_recognizer.get_intents_probabilities(intents, new_sentence)

print(intents_probabilities)

{'order_a_pizza': 0.51000000000000001, 'order_a_drink': 0.161}

# better performance can be achieved used tagged sentences

intents_probabilities = intent_recognizer.get_intents_probabilities(intents_pre_tagged, tagged_sentence)

print(intents_probabilities)

{'order_a_pizza': 0.53653846153846152, 'order_a_drink': 0.23928846153846156}

```