AUVision

Folgend ist eine Kette von Befehlen aufgelistet, die für spätere Projekte sinnvoll sein können:

In Python Console:

>>> import nltk

>>> nltk.download()

>>> from nltk.corpus import wordnet as wn

>>> print(wn.synset(‚dog.n.01‘).definition())

a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds

Wordnet NLTK Useage

Tokenize and Tag Text

>>> import nltk
>>> sentence = """I was feeling very odd this morning."""
>>> tokens = nltk.word_tokenize(sentence)
>>> tokens
['I', 'was', 'feeling', 'very', 'odd', 'this', 'morning', '.']
>>> tagged = nltk.pos_tag(tokens)
>>> tagged[0:8]
[('I', 'PRP'), ('was', 'VBD'), ('feeling', 'VBG'), ('very', 'RB'), 
('odd', 'RB'), ('this', 'DT'), ('morning', 'NN'), ('.', '.')]

Identify named entities
>>> entities = nltk.chunk.ne_chunk(tagged)
>>> entities
Tree('S', [('I', 'PRP'), ('was', 'VBD'), ('feeling', 'VBG'), ('very', 'RB'), 
('odd', 'RB'), ('this', 'DT'), ('morning', 'NN'), ('.', '.')])

Display a parse tree
>>> from nltk.corpus import treebank
>>> t = treebank.parsed_sents('wsj_0001.mrg')[0]
>>> t.draw()



>>> from nltk.parse.generate import generate, demo_grammar
>>> from nltk import CFG
>>> grammar = CFG.fromstring(demo_grammar)
>>> print(grammar)
Grammar with 13 productions (start state = S)
    S -> NP VP
    NP -> Det N
    PP -> P NP
    VP -> 'slept'
    VP -> 'saw' NP
    VP -> 'walked' PP
    Det -> 'the'
    Det -> 'a'
    N -> 'man'
    N -> 'park'
    N -> 'dog'
    P -> 'in'
    P -> 'with'

>>> for sentence in generate(grammar, depth=10): print(' '.join(sentence))
... 
the man slept
the man saw the man
the man saw the park
the man saw the dog
the man saw a man
the man saw a park
the man saw a dog
the man walked in the man
the man walked in the park
[...]
a dog walked with a park
a dog walked with a dog