nltk.tree module

Class for representing hierarchical language structures, such as syntax trees and morphological trees.

class nltk.tree.ImmutableProbabilisticTree[source]

Bases: nltk.tree.ImmutableTree, nltk.probability.ProbabilisticMixIn

__init__(node, children=None, **prob_kwargs)[source]
copy(deep=False)[source]

Return a shallow copy of the list.

classmethod convert(val)[source]
class nltk.tree.ImmutableTree[source]

Bases: nltk.tree.Tree

__init__(node, children=None)[source]
append(v)[source]

Append object to the end of the list.

extend(v)[source]

Extend list by appending elements from the iterable.

pop(v=None)[source]

Remove and return item at index (default last).

Raises IndexError if list is empty or index is out of range.

remove(v)[source]

Remove first occurrence of value.

Raises ValueError if the value is not present.

reverse()[source]

Reverse IN PLACE.

sort()[source]

Sort the list in ascending order and return None.

The sort is in-place (i.e. the list itself is modified) and stable (i.e. the order of two equal elements is maintained).

If a key function is given, apply it once to each list item and sort them, ascending or descending, according to their function values.

The reverse flag can be set to sort in descending order.

set_label(value)[source]

Set the node label. This will only succeed the first time the node label is set, which should occur in ImmutableTree.__init__().

class nltk.tree.ProbabilisticMixIn[source]

Bases: object

A mix-in class to associate probabilities with other classes (trees, rules, etc.). To use the ProbabilisticMixIn class, define a new class that derives from an existing class and from ProbabilisticMixIn. You will need to define a new constructor for the new class, which explicitly calls the constructors of both its parent classes. For example:

>>> from nltk.probability import ProbabilisticMixIn
>>> class A:
...     def __init__(self, x, y): self.data = (x,y)
...
>>> class ProbabilisticA(A, ProbabilisticMixIn):
...     def __init__(self, x, y, **prob_kwarg):
...         A.__init__(self, x, y)
...         ProbabilisticMixIn.__init__(self, **prob_kwarg)

See the documentation for the ProbabilisticMixIn constructor<__init__> for information about the arguments it expects.

You should generally also redefine the string representation methods, the comparison methods, and the hashing method.

__init__(**kwargs)[source]

Initialize this object’s probability. This initializer should be called by subclass constructors. prob should generally be the first argument for those constructors.

Parameters
  • prob (float) – The probability associated with the object.

  • logprob (float) – The log of the probability associated with the object.

set_prob(prob)[source]

Set the probability associated with this object to prob.

Parameters

prob (float) – The new probability

set_logprob(logprob)[source]

Set the log probability associated with this object to logprob. I.e., set the probability associated with this object to 2**(logprob).

Parameters

logprob (float) – The new log probability

prob()[source]

Return the probability associated with this object.

Return type

float

logprob()[source]

Return log(p), where p is the probability associated with this object.

Return type

float

class nltk.tree.ProbabilisticTree[source]

Bases: nltk.tree.Tree, nltk.probability.ProbabilisticMixIn

__init__(node, children=None, **prob_kwargs)[source]
copy(deep=False)[source]

Return a shallow copy of the list.

classmethod convert(val)[source]
class nltk.tree.Tree[source]

Bases: list

A Tree represents a hierarchical grouping of leaves and subtrees. For example, each constituent in a syntax tree is represented by a single Tree.

A tree’s children are encoded as a list of leaves and subtrees, where a leaf is a basic (non-tree) value; and a subtree is a nested Tree.

>>> from nltk.tree import Tree
>>> print(Tree(1, [2, Tree(3, [4]), 5]))
(1 2 (3 4) 5)
>>> vp = Tree('VP', [Tree('V', ['saw']),
...                  Tree('NP', ['him'])])
>>> s = Tree('S', [Tree('NP', ['I']), vp])
>>> print(s)
(S (NP I) (VP (V saw) (NP him)))
>>> print(s[1])
(VP (V saw) (NP him))
>>> print(s[1,1])
(NP him)
>>> t = Tree.fromstring("(S (NP I) (VP (V saw) (NP him)))")
>>> s == t
True
>>> t[1][1].set_label('X')
>>> t[1][1].label()
'X'
>>> print(t)
(S (NP I) (VP (V saw) (X him)))
>>> t[0], t[1,1] = t[1,1], t[0]
>>> print(t)
(S (X him) (VP (V saw) (NP I)))

The length of a tree is the number of children it has.

>>> len(t)
2

The set_label() and label() methods allow individual constituents to be labeled. For example, syntax trees use this label to specify phrase tags, such as “NP” and “VP”.

Several Tree methods use “tree positions” to specify children or descendants of a tree. Tree positions are defined as follows:

  • The tree position i specifies a Tree’s ith child.

  • The tree position () specifies the Tree itself.

  • If p is the tree position of descendant d, then p+i specifies the ith child of d.

I.e., every tree position is either a single index i, specifying tree[i]; or a sequence i1, i2, …, iN, specifying tree[i1][i2]...[iN].

Construct a new tree. This constructor can be called in one of two ways:

  • Tree(label, children) constructs a new tree with the

    specified label and list of children.

  • Tree.fromstring(s) constructs a new tree by parsing the string s.

__init__(node, children=None)[source]
property node

Outdated method to access the node value; use the label() method instead.

label()[source]

Return the node label of the tree.

>>> t = Tree.fromstring('(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))')
>>> t.label()
'S'
Returns

the node label (typically a string)

Return type

any

set_label(label)[source]

Set the node label of the tree.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.set_label("T")
>>> print(t)
(T (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))
Parameters

label (any) – the node label (typically a string)

leaves()[source]

Return the leaves of the tree.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.leaves()
['the', 'dog', 'chased', 'the', 'cat']
Returns

a list containing this tree’s leaves. The order reflects the order of the leaves in the tree’s hierarchical structure.

Return type

list

flatten()[source]

Return a flat version of the tree, with all non-root non-terminals removed.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> print(t.flatten())
(S the dog chased the cat)
Returns

a tree consisting of this tree’s root connected directly to its leaves, omitting all intervening non-terminal nodes.

Return type

Tree

height()[source]

Return the height of the tree.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.height()
5
>>> print(t[0,0])
(D the)
>>> t[0,0].height()
2
Returns

The height of this tree. The height of a tree containing no children is 1; the height of a tree containing only leaves is 2; and the height of any other tree is one plus the maximum of its children’s heights.

Return type

int

treepositions(order='preorder')[source]
>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.treepositions() 
[(), (0,), (0, 0), (0, 0, 0), (0, 1), (0, 1, 0), (1,), (1, 0), (1, 0, 0), ...]
>>> for pos in t.treepositions('leaves'):
...     t[pos] = t[pos][::-1].upper()
>>> print(t)
(S (NP (D EHT) (N GOD)) (VP (V DESAHC) (NP (D EHT) (N TAC))))
Parameters

order – One of: preorder, postorder, bothorder, leaves.

subtrees(filter=None)[source]

Generate all the subtrees of this tree, optionally restricted to trees matching the filter function.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> for s in t.subtrees(lambda t: t.height() == 2):
...     print(s)
(D the)
(N dog)
(V chased)
(D the)
(N cat)
Parameters

filter (function) – the function to filter all local trees

productions()[source]

Generate the productions that correspond to the non-terminal nodes of the tree. For each subtree of the form (P: C1 C2 … Cn) this produces a production of the form P -> C1 C2 … Cn.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.productions()
[S -> NP VP, NP -> D N, D -> 'the', N -> 'dog', VP -> V NP, V -> 'chased',
NP -> D N, D -> 'the', N -> 'cat']
Return type

list(Production)

pos()[source]

Return a sequence of pos-tagged words extracted from the tree.

>>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
>>> t.pos()
[('the', 'D'), ('dog', 'N'), ('chased', 'V'), ('the', 'D'), ('cat', 'N')]
Returns

a list of tuples containing leaves and pre-terminals (part-of-speech tags). The order reflects the order of the leaves in the tree’s hierarchical structure.

Return type

list(tuple)

leaf_treeposition(index)[source]
Returns

The tree position of the index-th leaf in this tree. I.e., if tp=self.leaf_treeposition(i), then self[tp]==self.leaves()[i].

Raises

IndexError – If this tree contains fewer than index+1 leaves, or if index<0.

treeposition_spanning_leaves(start, end)[source]
Returns

The tree position of the lowest descendant of this tree that dominates self.leaves()[start:end].

Raises

ValueError – if end <= start

chomsky_normal_form(factor='right', horzMarkov=None, vertMarkov=0, childChar='|', parentChar='^')[source]

This method can modify a tree in three ways:

  1. Convert a tree into its Chomsky Normal Form (CNF) equivalent – Every subtree has either two non-terminals or one terminal as its children. This process requires the creation of more”artificial” non-terminal nodes.

  2. Markov (vertical) smoothing of children in new artificial nodes

  3. Horizontal (parent) annotation of nodes

Parameters
  • factor (str = [left|right]) – Right or left factoring method (default = “right”)

  • horzMarkov (int | None) – Markov order for sibling smoothing in artificial nodes (None (default) = include all siblings)

  • vertMarkov (int | None) – Markov order for parent smoothing (0 (default) = no vertical annotation)

  • childChar (str) – A string used in construction of the artificial nodes, separating the head of the original subtree from the child nodes that have yet to be expanded (default = “|”)

  • parentChar (str) – A string used to separate the node representation from its vertical annotation

un_chomsky_normal_form(expandUnary=True, childChar='|', parentChar='^', unaryChar='+')[source]

This method modifies the tree in three ways:

  1. Transforms a tree in Chomsky Normal Form back to its original structure (branching greater than two)

  2. Removes any parent annotation (if it exists)

  3. (optional) expands unary subtrees (if previously collapsed with collapseUnary(…) )

Parameters
  • expandUnary (bool) – Flag to expand unary or not (default = True)

  • childChar (str) – A string separating the head node from its children in an artificial node (default = “|”)

  • parentChar (str) – A string separating the node label from its parent annotation (default = “^”)

  • unaryChar (str) – A string joining two non-terminals in a unary production (default = “+”)

collapse_unary(collapsePOS=False, collapseRoot=False, joinChar='+')[source]

Collapse subtrees with a single child (ie. unary productions) into a new non-terminal (Tree node) joined by ‘joinChar’. This is useful when working with algorithms that do not allow unary productions, and completely removing the unary productions would require loss of useful information. The Tree is modified directly (since it is passed by reference) and no value is returned.

Parameters
  • collapsePOS (bool) – ‘False’ (default) will not collapse the parent of leaf nodes (ie. Part-of-Speech tags) since they are always unary productions

  • collapseRoot (bool) – ‘False’ (default) will not modify the root production if it is unary. For the Penn WSJ treebank corpus, this corresponds to the TOP -> productions.

  • joinChar (str) – A string used to connect collapsed node values (default = “+”)

classmethod convert(tree)[source]

Convert a tree between different subtypes of Tree. cls determines which class will be used to encode the new tree.

Parameters

tree (Tree) – The tree that should be converted.

Returns

The new Tree.

copy(deep=False)[source]

Return a shallow copy of the list.

freeze(leaf_freezer=None)[source]
classmethod fromstring(s, brackets='()', read_node=None, read_leaf=None, node_pattern=None, leaf_pattern=None, remove_empty_top_bracketing=False)[source]

Read a bracketed tree string and return the resulting tree. Trees are represented as nested brackettings, such as:

(S (NP (NNP John)) (VP (V runs)))
Parameters
  • s (str) – The string to read

  • brackets (str (length=2)) – The bracket characters used to mark the beginning and end of trees and subtrees.

  • read_leaf (read_node,) –

    If specified, these functions are applied to the substrings of s corresponding to nodes and leaves (respectively) to obtain the values for those nodes and leaves. They should have the following signature:

    read_node(str) -> value

    For example, these functions could be used to process nodes and leaves whose values should be some type other than string (such as FeatStruct). Note that by default, node strings and leaf strings are delimited by whitespace and brackets; to override this default, use the node_pattern and leaf_pattern arguments.

  • leaf_pattern (node_pattern,) – Regular expression patterns used to find node and leaf substrings in s. By default, both nodes patterns are defined to match any sequence of non-whitespace non-bracket characters.

  • remove_empty_top_bracketing (bool) – If the resulting tree has an empty node label, and is length one, then return its single child instead. This is useful for treebank trees, which sometimes contain an extra level of bracketing.

Returns

A tree corresponding to the string representation s. If this class method is called using a subclass of Tree, then it will return a tree of that type.

Return type

Tree

classmethod fromlist(l)[source]
Parameters

l (list) – a tree represented as nested lists

Returns

A tree corresponding to the list representation l.

Return type

Tree

Convert nested lists to a NLTK Tree

draw()[source]

Open a new window containing a graphical diagram of this tree.

pretty_print(sentence=None, highlight=(), stream=None, **kwargs)[source]

Pretty-print this tree as ASCII or Unicode art. For explanation of the arguments, see the documentation for nltk.treeprettyprinter.TreePrettyPrinter.

pprint(**kwargs)[source]

Print a string representation of this Tree to ‘stream’

pformat(margin=70, indent=0, nodesep='', parens='()', quotes=False)[source]
Returns

A pretty-printed string representation of this tree.

Return type

str

Parameters
  • margin (int) – The right margin at which to do line-wrapping.

  • indent (int) – The indentation level at which printing begins. This number is used to decide how far to indent subsequent lines.

  • nodesep – A string that is used to separate the node from the children. E.g., the default value ':' gives trees like (S: (NP: I) (VP: (V: saw) (NP: it))).

pformat_latex_qtree()[source]

Returns a representation of the tree compatible with the LaTeX qtree package. This consists of the string \Tree followed by the tree represented in bracketed notation.

For example, the following result was generated from a parse tree of the sentence The announcement astounded us:

\Tree [.I'' [.N'' [.D The ] [.N' [.N announcement ] ] ]
    [.I' [.V'' [.V' [.V astounded ] [.N'' [.N' [.N us ] ] ] ] ] ] ]

See http://www.ling.upenn.edu/advice/latex.html for the LaTeX style file for the qtree package.

Returns

A latex qtree representation of this tree.

Return type

str

nltk.tree.bracket_parse(s)[source]

Use Tree.read(s, remove_empty_top_bracketing=True) instead.

nltk.tree.sinica_parse(s)[source]

Parse a Sinica Treebank string and return a tree. Trees are represented as nested brackettings, as shown in the following example (X represents a Chinese character): S(goal:NP(Head:Nep:XX)|theme:NP(Head:Nhaa:X)|quantity:Dab:X|Head:VL2:X)#0(PERIODCATEGORY)

Returns

A tree corresponding to the string representation.

Return type

Tree

Parameters

s (str) – The string to be converted

class nltk.tree.ParentedTree[source]

Bases: nltk.tree.AbstractParentedTree

A Tree that automatically maintains parent pointers for single-parented trees. The following are methods for querying the structure of a parented tree: parent, parent_index, left_sibling, right_sibling, root, treeposition.

Each ParentedTree may have at most one parent. In particular, subtrees may not be shared. Any attempt to reuse a single ParentedTree as a child of more than one parent (or as multiple children of the same parent) will cause a ValueError exception to be raised.

ParentedTrees should never be used in the same tree as Trees or MultiParentedTrees. Mixing tree implementations may result in incorrect parent pointers and in TypeError exceptions.

__init__(node, children=None)[source]
parent()[source]

The parent of this tree, or None if it has no parent.

parent_index()[source]

The index of this tree in its parent. I.e., ptree.parent()[ptree.parent_index()] is ptree. Note that ptree.parent_index() is not necessarily equal to ptree.parent.index(ptree), since the index() method returns the first child that is equal to its argument.

left_sibling()[source]

The left sibling of this tree, or None if it has none.

right_sibling()[source]

The right sibling of this tree, or None if it has none.

root()[source]

The root of this tree. I.e., the unique ancestor of this tree whose parent is None. If ptree.parent() is None, then ptree is its own root.

treeposition()[source]

The tree position of this tree, relative to the root of the tree. I.e., ptree.root[ptree.treeposition] is ptree.

class nltk.tree.MultiParentedTree[source]

Bases: nltk.tree.AbstractParentedTree

A Tree that automatically maintains parent pointers for multi-parented trees. The following are methods for querying the structure of a multi-parented tree: parents(), parent_indices(), left_siblings(), right_siblings(), roots, treepositions.

Each MultiParentedTree may have zero or more parents. In particular, subtrees may be shared. If a single MultiParentedTree is used as multiple children of the same parent, then that parent will appear multiple times in its parents() method.

MultiParentedTrees should never be used in the same tree as Trees or ParentedTrees. Mixing tree implementations may result in incorrect parent pointers and in TypeError exceptions.

__init__(node, children=None)[source]
parents()[source]

The set of parents of this tree. If this tree has no parents, then parents is the empty set. To check if a tree is used as multiple children of the same parent, use the parent_indices() method.

Type

list(MultiParentedTree)

left_siblings()[source]

A list of all left siblings of this tree, in any of its parent trees. A tree may be its own left sibling if it is used as multiple contiguous children of the same parent. A tree may appear multiple times in this list if it is the left sibling of this tree with respect to multiple parents.

Type

list(MultiParentedTree)

right_siblings()[source]

A list of all right siblings of this tree, in any of its parent trees. A tree may be its own right sibling if it is used as multiple contiguous children of the same parent. A tree may appear multiple times in this list if it is the right sibling of this tree with respect to multiple parents.

Type

list(MultiParentedTree)

roots()[source]

The set of all roots of this tree. This set is formed by tracing all possible parent paths until trees with no parents are found.

Type

list(MultiParentedTree)

parent_indices(parent)[source]

Return a list of the indices where this tree occurs as a child of parent. If this child does not occur as a child of parent, then the empty list is returned. The following is always true:

for parent_index in ptree.parent_indices(parent):
    parent[parent_index] is ptree
treepositions(root)[source]

Return a list of all tree positions that can be used to reach this multi-parented tree starting from root. I.e., the following is always true:

for treepos in ptree.treepositions(root):
    root[treepos] is ptree
class nltk.tree.ImmutableParentedTree[source]

Bases: nltk.tree.ImmutableTree, nltk.tree.ParentedTree

class nltk.tree.ImmutableMultiParentedTree[source]

Bases: nltk.tree.ImmutableTree, nltk.tree.MultiParentedTree