Esempi in Python per interpret_sents, esempi in Python per nltk.interpret_sents

Esempio n. 1

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def add_character_info(character_name: str, room_name: str, is_alive: bool, has_marks_on_neck: bool):
    if is_alive:
        s = [character_name + ' est vivant']
        print(s[0])
        a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_vivant.fcfg')))
    else:
        s = [character_name + ' est morte']
        print(s[0])
        a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_morte.fcfg')))

    if has_marks_on_neck:
        s = [character_name + ' a des marques au cou']
        print(s[0])
        a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_marque.fcfg')))

    s = [character_name + ' est dans le ' + room_name]
    print(s[0])
    a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_piece.fcfg')))

Esempio n. 2

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File: utils2.py Progetto: charlottesbrev/LT2213_temporary

def sem_parser(sents, syntax, verbose=False, is_cs=False):
    """
    It parses sentences with an FDFG grammar and returns a dictionary of 
    sentences to their semantic representations.
    
    Parameters:
    sents: a list of sentences to be parsed.
    fcfg_string: a string listing all fcfg rules with SEM for the 
                 FeatureGrammar.
    verbose: boolean value. default value is `False`. 
             if verbose is True it prints results.
    is_cs: boolean value. Inicating if it is using Cooper Storage. Default value is `False`. 
    Returns:
    dict: dictionary of sentences translated to a list of their 
          semantic representaions.
    """
    sents_reps = {sent: [] for sent in sents}
    for sent, results in zip(sents, nltk.interpret_sents(sents, syntax)):
        if verbose:
            display(Markdown(f"----\n{sent}: {len(results)} result(s)"))
        for j, (synrep, semrep) in enumerate(results):
            if is_cs:
                cs_semrep = cs.CooperStore(semrep)
                cs_semrep.s_retrieve(trace=False)
                for reading in cs_semrep.readings:
                    sents_reps[sent].append(reading)
                    if verbose:
                        display_latex(
                            reading)  # prints the SEM feature of a tree
                if verbose:
                    display_tree(synrep)  # show the parse tree
            else:
                sents_reps[sent].append(semrep)
                if verbose:
                    display_latex(semrep)  # prints the SEM feature of a tree
                    display_tree(synrep)  # show the parse tree
    return sents_reps

Esempio n. 3

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#example4('Jean tua Marie avec une corde dans la cuisine')


def example5(s1):
    ep = load_parser('635/quant01.fcfg')
    for tree in ep.parse(s1.split()):
        tree.draw()


#example5('tous les cochons sont gros')

sents1 = ['La victime a été tuée à 22h']
sents2 = ['Jean tua Marie avec une corde']
sents3 = ['une corde tua Marie avec une corde dans la cuisine']
sents4 = ['tous les cochons sont gros']


def printResults(results):
    for result in results:
        for (synrep, semrep) in result:
            print(semrep)


printResults(nltk.interpret_sents(sents1, 'heure_crime.fcfg'))
#printResults(nltk.interpret_sents(sents2, '635/sem02app.fcfg'))
#printResults(nltk.interpret_sents(sents3, '635/sem03app.fcfg'))
#printResults(nltk.interpret_sents(sents3, '635/sem04.fcfg'))
#printResults(nltk.interpret_sents(sents4, '635/quant01.fcfg'))

Esempio n. 4

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    def to_fol(self, fact, grammar):

        results = nltk.interpret_sents([fact,], grammar)
        sent = self.results_as_string(results)
        return sent

Esempio n. 5

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File: C10.py Progetto: zheng0115/NLTK-Python-CN

print(expr.simplify())  # β-约简

# simple-sem.fcfg包含了一个用于分析和翻译简单例子的小型规则集合
from nltk import load_parser

parser = load_parser('grammars/book_grammars/simple-sem.fcfg', trace=0)
parser = load_parser('grammars/book_grammars/simple-sem.fcfg', trace=2)
sentence = 'Angus gives a bone to every dog'
tokens = sentence.split()
for tree in parser.parse(tokens):
    print(tree.label()['SEM'])

# interpret_sents() 用于批量地解释输入的语句列表。输出的是语法表达（synrep）和语义表达（semrep）
sents = ['Irene walks', 'Cyril bites an ankle']
grammar_file = 'grammars/book_grammars/simple-sem.fcfg'
for results in nltk.interpret_sents(sents, grammar_file):
    for (synrep, semrep) in results:
        print("语法表达：")
        print(synrep)
        print("语义表达：")
        print(semrep)

# evaluate_sents() 用于批量地评估输入的语句列表，输出的是语法表达（synrep）、语义表达（semrep）和真值(value)
v = """
bertie=>b
olive=>o
cyril=>c
boy=>{b}
girl=>{o}
dog=>{c}
walk=>{o,c}

Esempio n. 6

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 def to_fol(self, fact, grammar):
     sent = self.results_as_string(nltk.interpret_sents(fact, grammar))
     # print(sent)
     return sent

Esempio n. 7

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