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128 lines
8.2 KiB
Plaintext
128 lines
8.2 KiB
Plaintext
.. Copyright (C) 2001-2019 NLTK Project
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.. For license information, see LICENSE.TXT
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========================================================
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Unit tests for nltk.treeprettyprinter.TreePrettyPrinter
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========================================================
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>>> from nltk.tree import Tree
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>>> from nltk.treeprettyprinter import TreePrettyPrinter
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Tree nr 2170 from nltk.corpus.treebank:
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>>> tree = Tree.fromstring(
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... '(S (NP-SBJ (PRP I)) (VP (VBP feel) (ADJP-PRD (RB pretty) '
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... '(JJ good)) (PP-CLR (IN about) (NP (PRP it)))) (. .))')
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>>> tpp = TreePrettyPrinter(tree)
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>>> print(tpp.text())
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S
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__________________________|_____________________
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| VP |
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| ____________________|___________ |
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| | | PP-CLR |
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| | | _____|_____ |
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NP-SBJ | ADJP-PRD | NP |
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| | _______|______ | | |
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PRP VBP RB JJ IN PRP .
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| | | | | | |
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I feel pretty good about it .
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>>> print(tpp.text(unicodelines=True))
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S
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┌──────────────────────────┼─────────────────────┐
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│ VP │
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│ ┌─────────────┬──────┴───────────┐ │
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│ │ │ PP-CLR │
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│ │ │ ┌─────┴─────┐ │
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NP-SBJ │ ADJP-PRD │ NP │
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│ │ ┌───────┴──────┐ │ │ │
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PRP VBP RB JJ IN PRP .
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│ │ │ │ │ │ │
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I feel pretty good about it .
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A tree with long labels:
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>>> tree = Tree.fromstring(
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... '(sentence (plural-noun-phrase (plural-noun Superconductors)) '
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... '(verb-phrase (plural-verb conduct) '
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... '(noun-phrase (singular-noun electricity))))')
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>>> tpp = TreePrettyPrinter(tree)
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>>> print(tpp.text(abbreviate=8, nodedist=2))
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sentence
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__________|__________
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| verb-phr.
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| __________|__________
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plural-n. | noun-phr.
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plural-n. plural-v. singular.
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Supercon. conduct electric.
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>>> print(tpp.text(maxwidth=8, nodedist=2))
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sentence
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_________|________
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| verb-
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| phrase
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| ________|_________
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plural- | noun-
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noun- | phrase
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phrase | |
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plural- plural- singular-
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noun verb noun
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Supercon conduct electric
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ductors ity
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A discontinuous tree:
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>>> tree = Tree.fromstring(
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... '(top (punct 8) (smain (noun 0) (verb 1) (inf (verb 5) (inf (verb 6) '
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... '(conj (inf (pp (prep 2) (np (det 3) (noun 4))) (verb 7)) (inf (verb 9)) '
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... '(vg 10) (inf (verb 11)))))) (punct 12))', read_leaf=int)
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>>> sentence = ('Ze had met haar moeder kunnen gaan winkelen ,'
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... ' zwemmen of terrassen .'.split())
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>>> tpp = TreePrettyPrinter(tree, sentence)
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>>> print(tpp.text())
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top
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_____|______________________________________________
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smain | |
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_______________________________|_____ | |
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| | inf | |
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| | _____|____ | |
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| | | inf | |
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| | | ____|_____ | |
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| | | | conj | |
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| | _____ | ___ | _________|______ | __________________ |
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| | inf | | | | | | |
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| | _________|_____ | ___ | _________ | | | | |
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| | pp | | | | | | | |
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| | ____|____ | | | | | | | |
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| | | np | | | | inf | inf |
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| | | ____|____ | | | | | | | |
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noun verb prep det noun verb verb verb punct verb vg verb punct
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Ze had met haar moeder kunnen gaan winkelen , zwemmen of terrassen .
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>>> print(tpp.text(unicodelines=True))
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top
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┌─────┴──────────────────┬───────────────────────────┐
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smain │ │
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┌────┬──────────────────────────┴─────┐ │ │
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│ │ inf │ │
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│ │ ┌─────┴────┐ │ │
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│ │ │ inf │ │
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│ │ │ ┌────┴─────┐ │ │
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│ │ │ │ conj │ │
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│ │ ┌───── │ ─── │ ─────────┴────── │ ─────┬─────┬──────┐ │
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│ │ inf │ │ │ │ │ │ │
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│ │ ┌─────────┴───── │ ─── │ ─────────┐ │ │ │ │ │
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│ │ pp │ │ │ │ │ │ │ │
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│ │ ┌────┴────┐ │ │ │ │ │ │ │ │
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│ │ │ np │ │ │ │ inf │ inf │
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│ │ │ ┌────┴────┐ │ │ │ │ │ │ │ │
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noun verb prep det noun verb verb verb punct verb vg verb punct
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│ │ │ │ │ │ │ │ │ │ │ │ │
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Ze had met haar moeder kunnen gaan winkelen , zwemmen of terrassen .
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