spaCy/spacy/syntax/nonproj.pyx
2018-02-26 12:02:01 +01:00

204 lines
7.1 KiB
Cython

# coding: utf-8
# cython: profile=True
# cython: infer_types=True
"""Implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005
for doing pseudo-projective parsing implementation uses the HEAD decoration
scheme.
"""
from __future__ import unicode_literals
from copy import copy
from ..tokens.doc cimport Doc
DELIMITER = '||'
def ancestors(tokenid, heads):
# Returns all words going from the word up the path to the root. The path
# to root cannot be longer than the number of words in the sentence. This
# function ends after at most len(heads) steps, because it would otherwise
# loop indefinitely on cycles.
head = tokenid
cnt = 0
while heads[head] != head and cnt < len(heads):
head = heads[head]
cnt += 1
yield head
if head is None:
break
def contains_cycle(heads):
# in an acyclic tree, the path from each word following the head relation
# upwards always ends at the root node
for tokenid in range(len(heads)):
seen = set([tokenid])
for ancestor in ancestors(tokenid, heads):
if ancestor in seen:
return seen
seen.add(ancestor)
return None
def is_nonproj_arc(tokenid, heads):
# definition (e.g. Havelka 2007): an arc h -> d, h < d is non-projective
# if there is a token k, h < k < d such that h is not
# an ancestor of k. Same for h -> d, h > d
head = heads[tokenid]
if head == tokenid: # root arcs cannot be non-projective
return False
elif head is None: # unattached tokens cannot be non-projective
return False
start, end = (head+1, tokenid) if head < tokenid else (tokenid+1, head)
for k in range(start, end):
for ancestor in ancestors(k, heads):
if ancestor is None: # for unattached tokens/subtrees
break
elif ancestor == head: # normal case: k dominated by h
break
else: # head not in ancestors: d -> h is non-projective
return True
return False
def is_nonproj_tree(heads):
# a tree is non-projective if at least one arc is non-projective
return any(is_nonproj_arc(word, heads) for word in range(len(heads)))
def decompose(label):
return label.partition(DELIMITER)[::2]
def is_decorated(label):
return label.find(DELIMITER) != -1
def preprocess_training_data(gold_tuples, label_freq_cutoff=30):
preprocessed = []
freqs = {}
for raw_text, sents in gold_tuples:
prepro_sents = []
for (ids, words, tags, heads, labels, iob), ctnts in sents:
proj_heads, deco_labels = projectivize(heads, labels)
# set the label to ROOT for each root dependent
deco_labels = ['ROOT' if head == i else deco_labels[i]
for i, head in enumerate(proj_heads)]
# count label frequencies
if label_freq_cutoff > 0:
for label in deco_labels:
if is_decorated(label):
freqs[label] = freqs.get(label, 0) + 1
prepro_sents.append(
((ids, words, tags, proj_heads, deco_labels, iob), ctnts))
preprocessed.append((raw_text, prepro_sents))
if label_freq_cutoff > 0:
return _filter_labels(preprocessed, label_freq_cutoff, freqs)
return preprocessed
def projectivize(heads, labels):
# Use the algorithm by Nivre & Nilsson 2005. Assumes heads to be a proper
# tree, i.e. connected and cycle-free. Returns a new pair (heads, labels)
# which encode a projective and decorated tree.
proj_heads = copy(heads)
smallest_np_arc = _get_smallest_nonproj_arc(proj_heads)
if smallest_np_arc is None: # this sentence is already projective
return proj_heads, copy(labels)
while smallest_np_arc is not None:
_lift(smallest_np_arc, proj_heads)
smallest_np_arc = _get_smallest_nonproj_arc(proj_heads)
deco_labels = _decorate(heads, proj_heads, labels)
return proj_heads, deco_labels
cpdef deprojectivize(Doc doc):
# Reattach arcs with decorated labels (following HEAD scheme). For each
# decorated arc X||Y, search top-down, left-to-right, breadth-first until
# hitting a Y then make this the new head.
for i in range(doc.length):
label = doc.vocab.strings[doc.c[i].dep]
if DELIMITER in label:
new_label, head_label = label.split(DELIMITER)
new_head = _find_new_head(doc[i], head_label)
doc[i].head = new_head
doc.c[i].dep = doc.vocab.strings.add(new_label)
return doc
def _decorate(heads, proj_heads, labels):
# uses decoration scheme HEAD from Nivre & Nilsson 2005
assert(len(heads) == len(proj_heads) == len(labels))
deco_labels = []
for tokenid, head in enumerate(heads):
if head != proj_heads[tokenid]:
deco_labels.append(
'%s%s%s' % (labels[tokenid], DELIMITER, labels[head]))
else:
deco_labels.append(labels[tokenid])
return deco_labels
def _get_smallest_nonproj_arc(heads):
# return the smallest non-proj arc or None
# where size is defined as the distance between dep and head
# and ties are broken left to right
smallest_size = float('inf')
smallest_np_arc = None
for tokenid, head in enumerate(heads):
size = abs(tokenid-head)
if size < smallest_size and is_nonproj_arc(tokenid, heads):
smallest_size = size
smallest_np_arc = tokenid
return smallest_np_arc
def _lift(tokenid, heads):
# reattaches a word to it's grandfather
head = heads[tokenid]
ghead = heads[head]
# attach to ghead if head isn't attached to root else attach to root
heads[tokenid] = ghead if head != ghead else tokenid
def _find_new_head(token, headlabel):
# search through the tree starting from the head of the given token
# returns the id of the first descendant with the given label
# if there is none, return the current head (no change)
queue = [token.head]
while queue:
next_queue = []
for qtoken in queue:
for child in qtoken.children:
if child.is_space:
continue
if child == token:
continue
if child.dep_ == headlabel:
return child
next_queue.append(child)
queue = next_queue
return token.head
def _filter_labels(gold_tuples, cutoff, freqs):
# throw away infrequent decorated labels
# can't learn them reliably anyway and keeps label set smaller
filtered = []
for raw_text, sents in gold_tuples:
filtered_sents = []
for (ids, words, tags, heads, labels, iob), ctnts in sents:
filtered_labels = []
for label in labels:
if is_decorated(label) and freqs.get(label, 0) < cutoff:
filtered_labels.append(decompose(label)[0])
else:
filtered_labels.append(label)
filtered_sents.append(
((ids, words, tags, heads, filtered_labels, iob), ctnts))
filtered.append((raw_text, filtered_sents))
return filtered