lang-model/naive-nlu/parsing.py

70 lines
2.1 KiB
Python

#!/usr/bin/env python
from functools import reduce
from knowledge_base import KnowledgeBase
def make_template(knowledge_base: KnowledgeBase, text, parsed):
tokens = text.split()
template = list(parsed)
for i in range(len(tokens)):
word = tokens[i]
if word in template:
print(word, i, template)
template[template.index(word)] = i
print(knowledge_base)
tokens[i] = {'groups': set(knowledge_base.knowledge[word]['groups'])}
return tokens, template
def integrate_language(knowledge_base: KnowledgeBase, example):
text = example["text"].lower()
parsed = example["parsed"]
matcher, result = make_template(knowledge_base, text, parsed)
print(text)
print(parsed)
print()
return matcher, result
def get_matching(sample, other):
l = len(sample[0])
other = list(filter(lambda x: len(x[0]) == l, other))
for i in range(l):
if len(other) == 0:
return []
if not isinstance(sample[0][i], str):
other = list(filter(lambda x: not isinstance(x[0][i], str) and
len(x[0][i]['groups'] & sample[0][i]['groups']) > 0,
other))
return [sample[0][x] if isinstance(sample[0][x], str)
else {'groups': sample[0][x]['groups'] & reduce(lambda a, b: a & b,
map(lambda y: y[0][x]['groups'],
other))}
for x
in range(l)]
def reprocess_language_knowledge(knowledge_base, examples):
examples = knowledge_base.examples + examples
print('\n'.join(map(str, knowledge_base.examples)))
print("--")
pattern_examples = []
for i, sample in enumerate(examples):
other = examples[:i] + examples[i + 1:]
print(sample)
match = get_matching(sample, other)
print("->", match)
if len(match) > 0:
sample = (match, sample[1],)
pattern_examples.append(sample)
print()
return pattern_examples