spaCy/spacy/nonproj.py
Wolfgang Seeker 4b2297d5d4 add class PseudoProjective for pseudo-projective parsing
PseudoProjective() implements the algorithm from Nivre & Nilsson 2005
using their HEAD decoration scheme.
2016-02-24 11:26:25 +01:00

182 lines
7.1 KiB
Python

from copy import copy
from collections import Counter
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 PseudoProjective:
# implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005
# for doing pseudo-projective parsing
# implementation uses the HEAD decoration scheme
def preprocess_training_data(self, labeled_trees, label_freq_cutoff=30):
# expects a sequence of pairs of head arrays and labels
preprocessed = []
for heads,labels in labeled_trees:
proj_heads,deco_labels = self.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) ]
preprocessed.append((proj_heads,deco_labels))
if label_freq_cutoff > 0:
return self._filter_labels(preprocessed,label_freq_cutoff)
return preprocessed
def projectivize(self, 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 = self._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:
self._lift(smallest_np_arc, proj_heads)
smallest_np_arc = self._get_smallest_nonproj_arc(proj_heads)
deco_labels = self._decorate(heads, proj_heads, labels)
return proj_heads, deco_labels
def deprojectivize(self, heads, labels):
# 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
newheads, newlabels = copy(heads), copy(labels)
spans = None
for tokenid, head in enumerate(heads):
if labels[tokenid].find('||') != -1:
newlabel,_,headlabel = labels[tokenid].partition('||')
newhead = self._find_new_head(head,tokenid,headlabel,heads,labels,spans=spans)
newheads[tokenid] = newhead
newlabels[tokenid] = newlabel
return newheads, newlabels
def _decorate(self, 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' % (labels[tokenid],labels[head]))
else:
deco_labels.append(labels[tokenid])
return deco_labels
def _get_smallest_nonproj_arc(self, 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(self, 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(self, rootid, tokenid, headlabel, heads, labels, spans=None):
# search through the tree starting from root
# returns the id of the first descendant with the given label
# if there is none, return the current head (no change)
if not spans:
spans = self._make_span_index(heads)
queue = spans.get(rootid,[])
queue.remove(tokenid) # don't search in the subtree of the nonproj arc
while queue:
next_queue = []
for idx in queue:
if labels[idx] == headlabel:
return idx
next_queue.extend(spans.get(idx,[]))
queue = next_queue
return heads[tokenid]
def _make_span_index(self, heads):
# stores the direct dependents for each token
# for searching top-down through a tree
spans = {}
for tokenid, head in enumerate(heads):
if tokenid == head: # root
continue
if head not in spans:
spans[head] = []
spans[head].append(tokenid)
return spans
def _filter_labels(self, labeled_trees, cutoff):
# throw away infrequent decorated labels
# can't learn them reliably anyway and keeps label set smaller
freqs = Counter([ label for _,labels in labeled_trees for label in labels if label.find('||') != -1 ])
filtered = []
for proj_heads,deco_labels in labeled_trees:
filtered_labels = [ label.partition('||')[0] if freqs.get(label,cutoff) < cutoff else label for label in deco_labels ]
filtered.append((proj_heads,filtered_labels))
return filtered