spaCy/spacy/syntax/nonproj.pyx

205 lines
7.7 KiB
Cython

# coding: utf-8
from __future__ import unicode_literals
from copy import copy
from ..tokens.doc cimport Doc
from ..attrs import DEP, HEAD
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 == 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 == 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 == 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)) )
class PseudoProjectivity:
# implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005
# for doing pseudo-projective parsing
# implementation uses the HEAD decoration scheme
delimiter = '||'
@classmethod
def decompose(cls, label):
return label.partition(cls.delimiter)[::2]
@classmethod
def is_decorated(cls, label):
return label.find(cls.delimiter) != -1
@classmethod
def preprocess_training_data(cls, 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 = cls.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 cls.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 cls._filter_labels(preprocessed,label_freq_cutoff,freqs)
return preprocessed
@classmethod
def projectivize(cls, 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 = cls._get_smallest_nonproj_arc(proj_heads)
if smallest_np_arc == None: # this sentence is already projective
return proj_heads, copy(labels)
while smallest_np_arc != None:
cls._lift(smallest_np_arc, proj_heads)
smallest_np_arc = cls._get_smallest_nonproj_arc(proj_heads)
deco_labels = cls._decorate(heads, proj_heads, labels)
return proj_heads, deco_labels
@classmethod
def deprojectivize(cls, tokens):
# 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
#parse = tokens.to_array([HEAD, DEP])
for token in tokens:
if cls.is_decorated(token.dep_):
newlabel,headlabel = cls.decompose(token.dep_)
newhead = cls._find_new_head(token,headlabel)
token.head = newhead
token.dep_ = newlabel
# tokens.attach(token,newhead,newlabel)
#parse[token.i,1] = tokens.vocab.strings[newlabel]
#parse[token.i,0] = newhead.i - token.i
#tokens.from_array([HEAD, DEP],parse)
@classmethod
def _decorate(cls, 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],cls.delimiter,labels[head]))
else:
deco_labels.append(labels[tokenid])
return deco_labels
@classmethod
def _get_smallest_nonproj_arc(cls, 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
@classmethod
def _lift(cls, 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
@classmethod
def _find_new_head(cls, 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
@classmethod
def _filter_labels(cls, 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 = [ cls.decompose(label)[0] if freqs.get(label,cutoff) < cutoff else label for label in labels ]
filtered_sents.append(((ids,words,tags,heads,filtered_labels,iob), ctnts))
filtered.append((raw_text, filtered_sents))
return filtered