53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
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import jsgf
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class Book_NLU: #Natural Language Understanding
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"""
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Moduł odpowiedzialny za analizę tekstu. W wyniku jego działania tekstowa reprezentacja wypowiedzi użytkownika zostaje zamieniona na jej reprezentację semantyczną, najczęściej w postaci ramy.
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Wejście: Tekst
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Wyjście: Akt użytkownika (rama)
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"""
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def __init__(self, acts, arguments, book_grammar):
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self.acts = acts
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self.arguments = arguments
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self.book_grammar = book_grammar
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def get_dialog_act(self, rule):
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slots = []
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self.get_slots(rule.expansion, slots)
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return {'act': rule.grammar.name, 'slots': slots}
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def get_slots(self, expansion, slots):
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if expansion.tag != '':
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slots.append((expansion.tag, expansion.current_match))
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return
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for child in expansion.children:
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self.get_slots(child, slots)
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if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):
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self.get_slots(expansion.referenced_rule.expansion, slots)
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def analyze(self, text):
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"""
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Analiza Tekstu wprowadzonego przez użytkownika i zamiana na akt (rama)
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"""
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print("Analiza Tekstu: " + text)
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act = "(greetings()&request(name))"
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print("Akt to: " + act)
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#przerobienie na wektor
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act_vector = [[0],[1,0]] #1 wektor to greetings, a 2 wektor to request z argumentem "name"
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print("Zamiana na: ")
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print(act_vector)
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return act_vector
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def test_nlu(self, utterance):
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matched = self.book_grammar.find_matching_rules(utterance)
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print(matched)
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if matched:
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return self.get_dialog_act(matched[0])
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else:
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return {'act': 'null', 'slots': []}
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