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