155 lines
4.7 KiB
Plaintext
155 lines
4.7 KiB
Plaintext
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.. Copyright (C) 2001-2023 NLTK Project
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.. For license information, see LICENSE.TXT
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-------------------------------------------
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Unit tests for the TreeTransformation class
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-------------------------------------------
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>>> from copy import deepcopy
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>>> from nltk.tree import Tree, collapse_unary, chomsky_normal_form, un_chomsky_normal_form
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>>> tree_string = "(TOP (S (S (VP (VBN Turned) (ADVP (RB loose)) (PP (IN in) (NP (NP (NNP Shane) (NNP Longman) (POS 's)) (NN trading) (NN room))))) (, ,) (NP (DT the) (NN yuppie) (NNS dealers)) (VP (AUX do) (NP (NP (RB little)) (ADJP (RB right)))) (. .)))"
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>>> tree = Tree.fromstring(tree_string)
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>>> print(tree)
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(TOP
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(S
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(S
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(VP
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(VBN Turned)
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(ADVP (RB loose))
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(PP
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(IN in)
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(NP
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(NP (NNP Shane) (NNP Longman) (POS 's))
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(NN trading)
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(NN room)))))
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(, ,)
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(NP (DT the) (NN yuppie) (NNS dealers))
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(VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
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(. .)))
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Make a copy of the original tree and collapse the subtrees with only one child
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>>> collapsedTree = deepcopy(tree)
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>>> collapse_unary(collapsedTree)
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>>> print(collapsedTree)
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(TOP
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(S
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(S+VP
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(VBN Turned)
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(ADVP (RB loose))
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(PP
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(IN in)
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(NP
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(NP (NNP Shane) (NNP Longman) (POS 's))
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(NN trading)
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(NN room))))
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(, ,)
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(NP (DT the) (NN yuppie) (NNS dealers))
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(VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
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(. .)))
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>>> collapsedTree2 = deepcopy(tree)
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>>> collapse_unary(collapsedTree2, collapsePOS=True, collapseRoot=True)
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>>> print(collapsedTree2)
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(TOP+S
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(S+VP
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(VBN Turned)
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(ADVP+RB loose)
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(PP
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(IN in)
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(NP
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(NP (NNP Shane) (NNP Longman) (POS 's))
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(NN trading)
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(NN room))))
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(, ,)
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(NP (DT the) (NN yuppie) (NNS dealers))
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(VP (AUX do) (NP (NP+RB little) (ADJP+RB right)))
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(. .))
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Convert the tree to Chomsky Normal Form i.e. each subtree has either two
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subtree children or a single leaf value. This conversion can be performed
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using either left- or right-factoring.
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>>> cnfTree = deepcopy(collapsedTree)
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>>> chomsky_normal_form(cnfTree, factor='left')
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>>> print(cnfTree)
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(TOP
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(S
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(S|<S+VP-,-NP-VP>
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(S|<S+VP-,-NP>
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(S|<S+VP-,>
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(S+VP
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(S+VP|<VBN-ADVP> (VBN Turned) (ADVP (RB loose)))
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(PP
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(IN in)
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(NP
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(NP|<NP-NN>
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(NP
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(NP|<NNP-NNP> (NNP Shane) (NNP Longman))
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(POS 's))
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(NN trading))
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(NN room))))
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(, ,))
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(NP (NP|<DT-NN> (DT the) (NN yuppie)) (NNS dealers)))
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(VP (AUX do) (NP (NP (RB little)) (ADJP (RB right)))))
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(. .)))
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>>> cnfTree = deepcopy(collapsedTree)
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>>> chomsky_normal_form(cnfTree, factor='right')
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>>> print(cnfTree)
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(TOP
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(S
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(S+VP
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(VBN Turned)
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(S+VP|<ADVP-PP>
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(ADVP (RB loose))
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(PP
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(IN in)
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(NP
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(NP (NNP Shane) (NP|<NNP-POS> (NNP Longman) (POS 's)))
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(NP|<NN-NN> (NN trading) (NN room))))))
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(S|<,-NP-VP-.>
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(, ,)
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(S|<NP-VP-.>
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(NP (DT the) (NP|<NN-NNS> (NN yuppie) (NNS dealers)))
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(S|<VP-.>
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(VP (AUX do) (NP (NP (RB little)) (ADJP (RB right))))
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(. .))))))
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Employ some Markov smoothing to make the artificial node labels a bit more
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readable. See the treetransforms.py documentation for more details.
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>>> markovTree = deepcopy(collapsedTree)
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>>> chomsky_normal_form(markovTree, horzMarkov=2, vertMarkov=1)
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>>> print(markovTree)
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(TOP
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(S^<TOP>
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(S+VP^<S>
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(VBN Turned)
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(S+VP|<ADVP-PP>^<S>
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(ADVP^<S+VP> (RB loose))
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(PP^<S+VP>
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(IN in)
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(NP^<PP>
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(NP^<NP>
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(NNP Shane)
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(NP|<NNP-POS>^<NP> (NNP Longman) (POS 's)))
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(NP|<NN-NN>^<PP> (NN trading) (NN room))))))
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(S|<,-NP>^<TOP>
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(, ,)
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(S|<NP-VP>^<TOP>
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(NP^<S> (DT the) (NP|<NN-NNS>^<S> (NN yuppie) (NNS dealers)))
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(S|<VP-.>^<TOP>
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(VP^<S>
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(AUX do)
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(NP^<VP> (NP^<NP> (RB little)) (ADJP^<NP> (RB right))))
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(. .))))))
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Convert the transformed tree back to its original form
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>>> un_chomsky_normal_form(markovTree)
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>>> tree == markovTree
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True
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