# Natural Language Toolkit: Combinatory Categorial Grammar
#
# Copyright (C) 2001-2013 NLTK Project
# Author: Graeme Gange <ggange@csse.unimelb.edu.au>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""
The lexicon is constructed by calling
``lexicon.parseLexicon(<lexicon string>)``.
In order to construct a parser, you also need a rule set.
The standard English rules are provided in chart as
``chart.DefaultRuleSet``.
The parser can then be constructed by calling, for example:
``parser = chart.CCGChartParser(<lexicon>, <ruleset>)``
Parsing is then performed by running
``parser.nbest_parse(<sentence>.split())``.
While this returns a list of trees, the default representation
of the produced trees is not very enlightening, particularly
given that it uses the same tree class as the CFG parsers.
It is probably better to call:
``chart.printCCGDerivation(<parse tree extracted from list>)``
which should print a nice representation of the derivation.
This entire process is shown far more clearly in the demonstration:
python chart.py
"""
from __future__ import print_function, division, unicode_literals
from nltk.parse import ParserI
from nltk.parse.chart import AbstractChartRule, EdgeI, Chart
from nltk.tree import Tree
from nltk.ccg.lexicon import parseLexicon
from nltk.ccg.combinator import (ForwardT, BackwardT, ForwardApplication,
BackwardApplication, ForwardComposition,
BackwardComposition, ForwardSubstitution,
BackwardBx, BackwardSx)
from nltk.compat import python_2_unicode_compatible, string_types
# Based on the EdgeI class from NLTK.
# A number of the properties of the EdgeI interface don't
# transfer well to CCGs, however.
[docs]class CCGEdge(EdgeI):
def __init__(self, span, categ, rule):
self._span = span
self._categ = categ
self._rule = rule
self._comparison_key = (span, categ, rule)
# Accessors
[docs] def lhs(self): return self._categ
[docs] def span(self): return self._span
[docs] def start(self): return self._span[0]
[docs] def end(self): return self._span[1]
[docs] def length(self): return self._span[1] - self.span[0]
[docs] def rhs(self): return ()
[docs] def dot(self): return 0
[docs] def is_complete(self): return True
[docs] def is_incomplete(self): return False
[docs] def nextsym(self): return None
[docs] def categ(self): return self._categ
[docs] def rule(self): return self._rule
[docs]class CCGLeafEdge(EdgeI):
'''
Class representing leaf edges in a CCG derivation.
'''
def __init__(self, pos, categ, leaf):
self._pos = pos
self._categ = categ
self._leaf = leaf
self._comparison_key = (pos, categ, leaf)
# Accessors
[docs] def lhs(self): return self._categ
[docs] def span(self): return (self._pos, self._pos+1)
[docs] def start(self): return self._pos
[docs] def end(self): return self._pos + 1
[docs] def length(self): return 1
[docs] def rhs(self): return self._leaf
[docs] def dot(self): return 0
[docs] def is_complete(self): return True
[docs] def is_incomplete(self): return False
[docs] def nextsym(self): return None
[docs] def categ(self): return self._categ
[docs] def leaf(self): return self._leaf
@python_2_unicode_compatible
[docs]class BinaryCombinatorRule(AbstractChartRule):
'''
Class implementing application of a binary combinator to a chart.
Takes the directed combinator to apply.
'''
NUMEDGES = 2
def __init__(self,combinator):
self._combinator = combinator
# Apply a combinator
[docs] def apply_iter(self, chart, grammar, left_edge, right_edge):
# The left & right edges must be touching.
if not (left_edge.end() == right_edge.start()):
return
# Check if the two edges are permitted to combine.
# If so, generate the corresponding edge.
if self._combinator.can_combine(left_edge.categ(),right_edge.categ()):
for res in self._combinator.combine(left_edge.categ(), right_edge.categ()):
new_edge = CCGEdge(span=(left_edge.start(), right_edge.end()),categ=res,rule=self._combinator)
if chart.insert(new_edge,(left_edge,right_edge)):
yield new_edge
# The representation of the combinator (for printing derivations)
def __str__(self):
return "%s" % self._combinator
# Type-raising must be handled slightly differently to the other rules, as the
# resulting rules only span a single edge, rather than both edges.
@python_2_unicode_compatible
[docs]class ForwardTypeRaiseRule(AbstractChartRule):
'''
Class for applying forward type raising
'''
NUMEDGES = 2
def __init__(self):
self._combinator = ForwardT
[docs] def apply_iter(self, chart, grammar, left_edge, right_edge):
if not (left_edge.end() == right_edge.start()):
return
for res in self._combinator.combine(left_edge.categ(), right_edge.categ()):
new_edge = CCGEdge(span=left_edge.span(),categ=res,rule=self._combinator)
if chart.insert(new_edge,(left_edge,)):
yield new_edge
def __str__(self):
return "%s" % self._combinator
@python_2_unicode_compatible
[docs]class BackwardTypeRaiseRule(AbstractChartRule):
'''
Class for applying backward type raising.
'''
NUMEDGES = 2
def __init__(self):
self._combinator = BackwardT
[docs] def apply_iter(self, chart, grammar, left_edge, right_edge):
if not (left_edge.end() == right_edge.start()):
return
for res in self._combinator.combine(left_edge.categ(), right_edge.categ()):
new_edge = CCGEdge(span=right_edge.span(),categ=res,rule=self._combinator)
if chart.insert(new_edge,(right_edge,)):
yield new_edge
def __str__(self):
return "%s" % self._combinator
# Common sets of combinators used for English derivations.
ApplicationRuleSet = [BinaryCombinatorRule(ForwardApplication),
BinaryCombinatorRule(BackwardApplication)]
CompositionRuleSet = [BinaryCombinatorRule(ForwardComposition),
BinaryCombinatorRule(BackwardComposition),
BinaryCombinatorRule(BackwardBx)]
SubstitutionRuleSet = [BinaryCombinatorRule(ForwardSubstitution),
BinaryCombinatorRule(BackwardSx)]
TypeRaiseRuleSet = [ForwardTypeRaiseRule(), BackwardTypeRaiseRule()]
# The standard English rule set.
DefaultRuleSet = ApplicationRuleSet + CompositionRuleSet + \
SubstitutionRuleSet + TypeRaiseRuleSet
[docs]class CCGChartParser(ParserI):
'''
Chart parser for CCGs.
Based largely on the ChartParser class from NLTK.
'''
def __init__(self, lexicon, rules, trace=0):
self._lexicon = lexicon
self._rules = rules
self._trace = trace
[docs] def lexicon(self):
return self._lexicon
# Implements the CYK algorithm
[docs] def nbest_parse(self, tokens, n=None):
tokens = list(tokens)
chart = CCGChart(list(tokens))
lex = self._lexicon
# Initialize leaf edges.
for index in range(chart.num_leaves()):
for cat in lex.categories(chart.leaf(index)):
new_edge = CCGLeafEdge(index, cat, chart.leaf(index))
chart.insert(new_edge, ())
# Select a span for the new edges
for span in range(2,chart.num_leaves()+1):
for start in range(0,chart.num_leaves()-span+1):
# Try all possible pairs of edges that could generate
# an edge for that span
for part in range(1,span):
lstart = start
mid = start + part
rend = start + span
for left in chart.select(span=(lstart,mid)):
for right in chart.select(span=(mid,rend)):
# Generate all possible combinations of the two edges
for rule in self._rules:
edges_added_by_rule = 0
for newedge in rule.apply_iter(chart,lex,left,right):
edges_added_by_rule += 1
# Output the resulting parses
return chart.parses(lex.start())[:n]
[docs]class CCGChart(Chart):
def __init__(self, tokens):
Chart.__init__(self, tokens)
# Constructs the trees for a given parse. Unfortnunately, the parse trees need to be
# constructed slightly differently to those in the default Chart class, so it has to
# be reimplemented
def _trees(self, edge, complete, memo, tree_class):
if edge in memo:
return memo[edge]
trees = []
memo[edge] = []
if isinstance(edge,CCGLeafEdge):
word = tree_class(edge.lhs(),[self._tokens[edge.start()]])
leaf = tree_class((edge.lhs(),"Leaf"),[word])
memo[edge] = leaf
return leaf
for cpl in self.child_pointer_lists(edge):
child_choices = [self._trees(cp, complete, memo, tree_class)
for cp in cpl]
if len(child_choices) > 0 and isinstance(child_choices[0], string_types):
child_choices = [child_choices]
for children in self._choose_children(child_choices):
lhs = (edge.lhs(), "%s" % edge.rule())
trees.append(tree_class(lhs, children))
memo[edge] = trees
return trees
#--------
# Displaying derivations
#--------
[docs]def printCCGDerivation(tree):
# Get the leaves and initial categories
leafcats = tree.pos()
leafstr = ''
catstr = ''
# Construct a string with both the leaf word and corresponding
# category aligned.
for (leaf, cat) in leafcats:
str_cat = "%s" % cat
# print(cat.__class__)
# print("str_cat", str_cat)
nextlen = 2 + max(len(leaf), len(str_cat))
lcatlen = (nextlen - len(str_cat)) // 2
rcatlen = lcatlen + (nextlen - len(str_cat)) % 2
catstr += ' '*lcatlen + str_cat + ' '*rcatlen
lleaflen = (nextlen - len(leaf)) // 2
rleaflen = lleaflen + (nextlen - len(leaf)) % 2
leafstr += ' '*lleaflen + leaf + ' '*rleaflen
print(leafstr)
print(catstr)
# Display the derivation steps
printCCGTree(0,tree)
# Prints the sequence of derivation steps.
[docs]def printCCGTree(lwidth,tree):
rwidth = lwidth
# Is a leaf (word).
# Increment the span by the space occupied by the leaf.
if not isinstance(tree,Tree):
return 2 + lwidth + len(tree)
# Find the width of the current derivation step
for child in tree:
rwidth = max(rwidth, printCCGTree(rwidth,child))
# Is a leaf node.
# Don't print anything, but account for the space occupied.
if not isinstance(tree.label(), tuple):
return max(rwidth,2 + lwidth + len("%s" % tree.label()),
2 + lwidth + len(tree[0]))
(res,op) = tree.label()
# Pad to the left with spaces, followed by a sequence of '-'
# and the derivation rule.
print(lwidth*' ' + (rwidth-lwidth)*'-' + "%s" % op)
# Print the resulting category on a new line.
str_res = "%s" % res
respadlen = (rwidth - lwidth - len(str_res)) // 2 + lwidth
print(respadlen*' ' + str_res)
return rwidth
### Demonstration code
# Construct the lexicon
lex = parseLexicon('''
:- S, NP, N, VP # Primitive categories, S is the target primitive
Det :: NP/N # Family of words
Pro :: NP
TV :: VP/NP
Modal :: (S\\NP)/VP # Backslashes need to be escaped
I => Pro # Word -> Category mapping
you => Pro
the => Det
# Variables have the special keyword 'var'
# '.' prevents permutation
# ',' prevents composition
and => var\\.,var/.,var
which => (N\\N)/(S/NP)
will => Modal # Categories can be either explicit, or families.
might => Modal
cook => TV
eat => TV
mushrooms => N
parsnips => N
bacon => N
''')
[docs]def demo():
parser = CCGChartParser(lex, DefaultRuleSet)
for parse in parser.nbest_parse("I might cook and eat the bacon".split(), 3):
printCCGDerivation(parse)
if __name__ == '__main__':
demo()