331 lines
11 KiB
Python
331 lines
11 KiB
Python
# Natural Language Toolkit: API for alignment and translation objects
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Will Zhang <wilzzha@gmail.com>
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# Guan Gui <ggui@student.unimelb.edu.au>
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# Steven Bird <stevenbird1@gmail.com>
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# Tah Wei Hoon <hoon.tw@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import print_function, unicode_literals
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import subprocess
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from collections import namedtuple
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from nltk.compat import python_2_unicode_compatible
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@python_2_unicode_compatible
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class AlignedSent(object):
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"""
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Return an aligned sentence object, which encapsulates two sentences
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along with an ``Alignment`` between them.
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Typically used in machine translation to represent a sentence and
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its translation.
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>>> from nltk.translate import AlignedSent, Alignment
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>>> algnsent = AlignedSent(['klein', 'ist', 'das', 'Haus'],
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... ['the', 'house', 'is', 'small'], Alignment.fromstring('0-3 1-2 2-0 3-1'))
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>>> algnsent.words
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['klein', 'ist', 'das', 'Haus']
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>>> algnsent.mots
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['the', 'house', 'is', 'small']
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>>> algnsent.alignment
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Alignment([(0, 3), (1, 2), (2, 0), (3, 1)])
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>>> from nltk.corpus import comtrans
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>>> print(comtrans.aligned_sents()[54])
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<AlignedSent: 'Weshalb also sollten...' -> 'So why should EU arm...'>
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>>> print(comtrans.aligned_sents()[54].alignment)
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0-0 0-1 1-0 2-2 3-4 3-5 4-7 5-8 6-3 7-9 8-9 9-10 9-11 10-12 11-6 12-6 13-13
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:param words: Words in the target language sentence
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:type words: list(str)
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:param mots: Words in the source language sentence
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:type mots: list(str)
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:param alignment: Word-level alignments between ``words`` and ``mots``.
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Each alignment is represented as a 2-tuple (words_index, mots_index).
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:type alignment: Alignment
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"""
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def __init__(self, words, mots, alignment=None):
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self._words = words
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self._mots = mots
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if alignment is None:
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self.alignment = Alignment([])
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else:
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assert type(alignment) is Alignment
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self.alignment = alignment
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@property
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def words(self):
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return self._words
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@property
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def mots(self):
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return self._mots
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def _get_alignment(self):
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return self._alignment
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def _set_alignment(self, alignment):
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_check_alignment(len(self.words), len(self.mots), alignment)
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self._alignment = alignment
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alignment = property(_get_alignment, _set_alignment)
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def __repr__(self):
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"""
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Return a string representation for this ``AlignedSent``.
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:rtype: str
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"""
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words = "[%s]" % (", ".join("'%s'" % w for w in self._words))
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mots = "[%s]" % (", ".join("'%s'" % w for w in self._mots))
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return "AlignedSent(%s, %s, %r)" % (words, mots, self._alignment)
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def _to_dot(self):
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"""
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Dot representation of the aligned sentence
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"""
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s = 'graph align {\n'
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s += 'node[shape=plaintext]\n'
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# Declare node
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for w in self._words:
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s += '"%s_source" [label="%s"] \n' % (w, w)
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for w in self._mots:
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s += '"%s_target" [label="%s"] \n' % (w, w)
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# Alignment
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for u, v in self._alignment:
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s += '"%s_source" -- "%s_target" \n' % (self._words[u], self._mots[v])
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# Connect the source words
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for i in range(len(self._words) - 1):
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s += '"%s_source" -- "%s_source" [style=invis]\n' % (self._words[i], self._words[i + 1])
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# Connect the target words
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for i in range(len(self._mots) - 1):
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s += '"%s_target" -- "%s_target" [style=invis]\n' % (self._mots[i], self._mots[i + 1])
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# Put it in the same rank
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s += '{rank = same; %s}\n' % (' '.join('"%s_source"' % w for w in self._words))
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s += '{rank = same; %s}\n' % (' '.join('"%s_target"' % w for w in self._mots))
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s += '}'
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return s
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def _repr_svg_(self):
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"""
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Ipython magic : show SVG representation of this ``AlignedSent``.
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"""
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dot_string = self._to_dot().encode('utf8')
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output_format = 'svg'
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try:
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process = subprocess.Popen(['dot', '-T%s' % output_format], stdin=subprocess.PIPE,
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stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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except OSError:
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raise Exception('Cannot find the dot binary from Graphviz package')
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out, err = process.communicate(dot_string)
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return out.decode('utf8')
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def __str__(self):
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"""
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Return a human-readable string representation for this ``AlignedSent``.
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:rtype: str
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"""
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source = " ".join(self._words)[:20] + "..."
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target = " ".join(self._mots)[:20] + "..."
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return "<AlignedSent: '%s' -> '%s'>" % (source, target)
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def invert(self):
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"""
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Return the aligned sentence pair, reversing the directionality
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:rtype: AlignedSent
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"""
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return AlignedSent(self._mots, self._words,
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self._alignment.invert())
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@python_2_unicode_compatible
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class Alignment(frozenset):
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"""
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A storage class for representing alignment between two sequences, s1, s2.
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In general, an alignment is a set of tuples of the form (i, j, ...)
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representing an alignment between the i-th element of s1 and the
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j-th element of s2. Tuples are extensible (they might contain
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additional data, such as a boolean to indicate sure vs possible alignments).
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>>> from nltk.translate import Alignment
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>>> a = Alignment([(0, 0), (0, 1), (1, 2), (2, 2)])
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>>> a.invert()
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Alignment([(0, 0), (1, 0), (2, 1), (2, 2)])
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>>> print(a.invert())
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0-0 1-0 2-1 2-2
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>>> a[0]
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[(0, 1), (0, 0)]
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>>> a.invert()[2]
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[(2, 1), (2, 2)]
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>>> b = Alignment([(0, 0), (0, 1)])
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>>> b.issubset(a)
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True
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>>> c = Alignment.fromstring('0-0 0-1')
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>>> b == c
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True
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"""
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def __new__(cls, pairs):
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self = frozenset.__new__(cls, pairs)
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self._len = (max(p[0] for p in self) if self != frozenset([]) else 0)
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self._index = None
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return self
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@classmethod
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def fromstring(cls, s):
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"""
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Read a giza-formatted string and return an Alignment object.
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>>> Alignment.fromstring('0-0 2-1 9-2 21-3 10-4 7-5')
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Alignment([(0, 0), (2, 1), (7, 5), (9, 2), (10, 4), (21, 3)])
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:type s: str
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:param s: the positional alignments in giza format
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:rtype: Alignment
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:return: An Alignment object corresponding to the string representation ``s``.
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"""
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return Alignment([_giza2pair(a) for a in s.split()])
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def __getitem__(self, key):
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"""
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Look up the alignments that map from a given index or slice.
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"""
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if not self._index:
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self._build_index()
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return self._index.__getitem__(key)
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def invert(self):
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"""
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Return an Alignment object, being the inverted mapping.
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"""
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return Alignment(((p[1], p[0]) + p[2:]) for p in self)
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def range(self, positions=None):
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"""
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Work out the range of the mapping from the given positions.
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If no positions are specified, compute the range of the entire mapping.
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"""
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image = set()
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if not self._index:
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self._build_index()
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if not positions:
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positions = list(range(len(self._index)))
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for p in positions:
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image.update(f for _, f in self._index[p])
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return sorted(image)
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def __repr__(self):
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"""
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Produce a Giza-formatted string representing the alignment.
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"""
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return "Alignment(%r)" % sorted(self)
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def __str__(self):
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"""
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Produce a Giza-formatted string representing the alignment.
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"""
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return " ".join("%d-%d" % p[:2] for p in sorted(self))
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def _build_index(self):
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"""
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Build a list self._index such that self._index[i] is a list
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of the alignments originating from word i.
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"""
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self._index = [[] for _ in range(self._len + 1)]
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for p in self:
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self._index[p[0]].append(p)
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def _giza2pair(pair_string):
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i, j = pair_string.split("-")
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return int(i), int(j)
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def _naacl2pair(pair_string):
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i, j, p = pair_string.split("-")
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return int(i), int(j)
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def _check_alignment(num_words, num_mots, alignment):
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"""
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Check whether the alignments are legal.
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:param num_words: the number of source language words
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:type num_words: int
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:param num_mots: the number of target language words
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:type num_mots: int
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:param alignment: alignment to be checked
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:type alignment: Alignment
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:raise IndexError: if alignment falls outside the sentence
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"""
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assert type(alignment) is Alignment
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if not all(0 <= pair[0] < num_words for pair in alignment):
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raise IndexError("Alignment is outside boundary of words")
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if not all(pair[1] is None or 0 <= pair[1] < num_mots for pair in alignment):
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raise IndexError("Alignment is outside boundary of mots")
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PhraseTableEntry = namedtuple('PhraseTableEntry', ['trg_phrase', 'log_prob'])
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class PhraseTable(object):
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"""
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In-memory store of translations for a given phrase, and the log
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probability of the those translations
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"""
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def __init__(self):
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self.src_phrases = dict()
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def translations_for(self, src_phrase):
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"""
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Get the translations for a source language phrase
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:param src_phrase: Source language phrase of interest
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:type src_phrase: tuple(str)
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:return: A list of target language phrases that are translations
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of ``src_phrase``, ordered in decreasing order of
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likelihood. Each list element is a tuple of the target
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phrase and its log probability.
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:rtype: list(PhraseTableEntry)
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"""
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return self.src_phrases[src_phrase]
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def add(self, src_phrase, trg_phrase, log_prob):
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"""
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:type src_phrase: tuple(str)
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:type trg_phrase: tuple(str)
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:param log_prob: Log probability that given ``src_phrase``,
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``trg_phrase`` is its translation
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:type log_prob: float
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"""
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entry = PhraseTableEntry(trg_phrase=trg_phrase, log_prob=log_prob)
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if src_phrase not in self.src_phrases:
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self.src_phrases[src_phrase] = []
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self.src_phrases[src_phrase].append(entry)
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self.src_phrases[src_phrase].sort(key=lambda e: e.log_prob,
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reverse=True)
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def __contains__(self, src_phrase):
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return src_phrase in self.src_phrases
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