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* Use updated JSON format, with sentences below paragraphs. Allows use of gold preprocessing flag.

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This commit is contained in:
Matthew Honnibal 2015-05-30 01:25:46 +02:00
parent 2d11739f28
commit 76300bbb1b
4 changed files with 85 additions and 67 deletions
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65
bin/parser/train.py
View File

@ -81,21 +81,21 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, annot_tuples, ctnt in gold_tuples:
score_model(scorer, nlp, raw_text, annot_tuples)
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
try:
loss += nlp.parser.train(tokens, gold)
except AssertionError:
# TODO: Do something about non-projective sentences
pass
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
for raw_text, sents in gold_tuples:
if not gold_preproc:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
score_model(scorer, nlp, raw_text, annot_tuples)
if raw_text is None or gold_preproc:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
if gold.is_projective:
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print '%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
@ -107,19 +107,21 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=True):
assert not gold_preproc
nlp = Language(data_dir=model_dir)
scorer = Scorer()
for raw_text, annot_tuples, brackets in gold_tuples:
if raw_text is not None:
tokens = nlp(raw_text, merge_mwes=False)
else:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
for raw_text, sents in gold_tuples:
for annot_tuples, brackets in sents:
if raw_text is None or gold_preproc:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
else:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
for t in tokens:
print t.orth_, t.dep_, t.head.orth_, t.ent_type_
return scorer
@ -141,6 +143,7 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
train_loc=("Location of training file or directory"),
dev_loc=("Location of development file or directory"),
corruption_level=("Amount of noise to add to training data", "option", "c", float),
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
model_dir=("Location of output model directory",),
out_loc=("Out location", "option", "o", str),
n_sents=("Number of training sentences", "option", "n", int),
@ -149,16 +152,16 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
debug=("Debug mode", "flag", "d", bool)
)
def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0):
debug=False, corruption_level=0.0, gold_preproc=False):
gold_train = list(read_json_file(train_loc))
train(English, gold_train, model_dir,
feat_set='basic' if not debug else 'debug',
gold_preproc=False, n_sents=n_sents,
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter)
if out_loc:
write_parses(English, dev_loc, model_dir, out_loc)
#if out_loc:
# write_parses(English, dev_loc, model_dir, out_loc)
scorer = evaluate(English, list(read_json_file(dev_loc)),
model_dir, gold_preproc=False, verbose=verbose)
model_dir, gold_preproc=gold_preproc, verbose=verbose)
print 'TOK', 100-scorer.token_acc
print 'POS', scorer.tags_acc
print 'UAS', scorer.uas

50
spacy/gold.pyx
View File

@ -104,24 +104,25 @@ def read_json_file(loc):
for doc in ijson.items(file_, 'item'):
paragraphs = []
for paragraph in doc['paragraphs']:
words = []
ids = []
tags = []
heads = []
labels = []
ner = []
for token in paragraph['tokens']:
words.append(token['orth'])
ids.append(token['id'])
tags.append(token['tag'])
heads.append(token['head'] if token['head'] >= 0 else token['id'])
labels.append(token['dep'])
ner.append(token.get('ner', '-'))
yield (
paragraph.get('raw', None),
(ids, words, tags, heads, labels, ner),
paragraph.get('brackets', []))
sents = []
for sent in paragraph['sentences']:
words = []
ids = []
tags = []
heads = []
labels = []
ner = []
for i, token in enumerate(sent['tokens']):
words.append(token['orth'])
ids.append(i)
tags.append(token['tag'])
heads.append(token['head'] + i)
labels.append(token['dep'])
ner.append(token.get('ner', '-'))
sents.append((
(ids, words, tags, heads, labels, ner),
sent.get('brackets', [])))
yield (paragraph.get('raw', None), sents)
def _iob_to_biluo(tags):
@ -203,6 +204,19 @@ cdef class GoldParse:
def __len__(self):
return self.length
@property
def is_projective(self):
heads = [head for (id_, word, tag, head, dep, ner) in self.orig_annot]
deps = sorted([sorted(arc) for arc in enumerate(heads)])
for w1, h1 in deps:
for w2, h2 in deps:
if w1 < w2 < h1 < h2:
return False
elif w1 < w2 == h2 < h1:
return False
else:
return True
def is_punct_label(label):
return label == 'P' or label.lower() == 'punct'

19
spacy/syntax/arc_eager.pyx
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@ -54,15 +54,16 @@ cdef class ArcEager(TransitionSystem):
move_labels = {SHIFT: {'': True}, REDUCE: {'': True}, RIGHT: {},
LEFT: {'ROOT': True}, BREAK: {'ROOT': True},
CONSTITUENT: {}, ADJUST: {'': True}}
for raw_text, (ids, words, tags, heads, labels, iob), ctnts in gold_parses:
for child, head, label in zip(ids, heads, labels):
if label != 'ROOT':
if head < child:
move_labels[RIGHT][label] = True
elif head > child:
move_labels[LEFT][label] = True
for start, end, label in ctnts:
move_labels[CONSTITUENT][label] = True
for raw_text, sents in gold_parses:
for (ids, words, tags, heads, labels, iob), ctnts in sents:
for child, head, label in zip(ids, heads, labels):
if label != 'ROOT':
if head < child:
move_labels[RIGHT][label] = True
elif head > child:
move_labels[LEFT][label] = True
for start, end, label in ctnts:
move_labels[CONSTITUENT][label] = True
return move_labels
cdef int preprocess_gold(self, GoldParse gold) except -1:

18
spacy/syntax/ner.pyx
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@ -73,15 +73,15 @@ cdef class BiluoPushDown(TransitionSystem):
move_labels = {MISSING: {'': True}, BEGIN: {}, IN: {}, LAST: {}, UNIT: {},
OUT: {'': True}}
moves = ('M', 'B', 'I', 'L', 'U')
for (raw_text, tuples, ctnt) in gold_tuples:
ids, words, tags, heads, labels, biluo = tuples
for i, ner_tag in enumerate(biluo):
if ner_tag != 'O' and ner_tag != '-':
if ner_tag.count('-') != 1:
raise ValueError(ner_tag)
_, label = ner_tag.split('-')
for move_str in ('B', 'I', 'L', 'U'):
move_labels[moves.index(move_str)][label] = True
for raw_text, sents in gold_tuples:
for (ids, words, tags, heads, labels, biluo), _ in sents:
for i, ner_tag in enumerate(biluo):
if ner_tag != 'O' and ner_tag != '-':
if ner_tag.count('-') != 1:
raise ValueError(ner_tag)
_, label = ner_tag.split('-')
for move_str in ('B', 'I', 'L', 'U'):
move_labels[moves.index(move_str)][label] = True
return move_labels
def move_name(self, int move, int label):