GETTING_STARTED.md

Adapt Intent Determination

The Adapt Intent Parser is a flexible and extensible intent definition and determination framework. It is intended to parse natural language text into a structured intent that can then be invoked programmatically. It uses a technique known as known-entity tagging to tag pre-defined vocabulary within utterances. These tags can then be passed to an intent definition for validation.

Getting Started

The Parser depends on a tokenizer (splits an utterance into tokens), and a prefix tree loaded with vocabulary.

    # Instantiate a Trie for in-memory index, a tokenizer, and the entity tagger.
    trie = Trie()
    tokenizer = EnglishTokenizer()
    tagger = EntityTagger(self.trie, self.tokenizer)
    # load vocabulary into index
    trie.insert("play", "PlayVerb")
    trie.insert("the big bang theory", "Television Show")
    trie.insert("the big", "Not a Thing")
    trie.insert("barenaked ladies", "Radio Station")
    # Instantiate parser
    parser = Parser(self.tokenizer, self.tagger)

    # Define intent
    intent = IntentBuilder("play television intent")\
        .require("PlayVerb")\
        .require("Television Show")\
        .build()

    # parser.parse returns a generator, which returns (using a greedy algo) highest confidence parse first, for short-circuiting
    for result in self.parser.parse("play the big bang theory"):
        # validate an intent. A score of 0.0 indicates the parse result is not a valid representation of the intent.
        result_intent = intent.validate(result.get('tags'), result.get('confidence'))
        assert result_intent.get('confidence') > 0.0
        assert result_intent.get('PlayVerb') == 'play'
        assert result_intent.get('Television Show') == "the big bang theory"