def build_target_dicts(targetdictFP, unifreqFP, dataFP='../scoring/cv.train.sentences.json'):
uniFreq = Counter()
targets = Counter()
with codecs.open(dataFP, 'r', 'utf-8') as inF:
for ln in inF:
sentence = json.loads(ln)
# unigrams
#print(sentence['tokens'],sentence['pos'], file=sys.stderr)
if len(sentence['tokens'])!=len(sentence['pos']):
# sometimes the data is buggy and missing a POS tag :(
# stopgap: insert ? as the tag
# TODO: fix this in preprocessing, make an assert here
posmap = {stuff['start']: stuff for stuff in sentence['pos']}
sentence['pos'] = [posmap.get(i, {'start': i, 'end': i+1, 'name': '?', 'text': sentence['tokens'][i]}) for i in range(len(sentence['tokens']))]
tokenOffsets = set() # tokens of interest: either part of a target or present in the WSL
for wslentry in sentence['wsl']:
tokenOffsets |= {i for i in range(wslentry['start'],wslentry['end'])}
# targets
for frame in sentence['frames']:
target_toks = {i for span in frame['target']['spans'] for i in range(span['start'],span['end'])}
tokenOffsets |= target_toks
lemmas = [get_lemma(entry['text'], entry['name'])+'_'+entry['name'].upper()[0] for i in sorted(target_toks) for entry in [sentence['pos'][i]]]
targets[' '.join(lemmas)] += 1
# unigram counts
# ignore tokens tagged as proper nouns (consequence: if there are proper noun unigram targets,
# the target/unigram ratio can be greater than 1)
uniFreq.update(get_lemma(entry['text'], entry['name'])+'_'+entry['name'].upper()[0]
for entry in sentence['pos'] if entry['start'] in tokenOffsets and not entry['name'].startswith('NP') and not entry['name'].startswith('NNP'))
with codecs.open(unifreqFP, 'w', 'utf-8') as outF:
for w,n in uniFreq.items():
outF.write(w+'\t'+str(n)+'\n')
with codecs.open(targetdictFP, 'w', 'utf-8') as outF:
for ww,n in targets.items():
outF.write(ww+'\t'+str(n)+'\n')
def extract_features(conll_tokens):
for t in conll_tokens:
t.lemma = get_lemma(t.form, t.postag)
t.cpostag = get_coarse_pos(t.postag)
with_walls = [LEFT_ANCHOR] + conll_tokens + [RIGHT_ANCHOR]
trigrams = ngrams(with_walls, 3)
for trigram in trigrams:
before, this, after = trigram
yield {
'lemma_%s' % this.lemma: True,
'pos_%s' % this.cpostag: True,
'prev_lemma_%s' % before.lemma: True,
'prev_pos_%s' % before.cpostag: True,
'next_lemma_%s' % after.lemma: True,
'next_pos_%s' % after.cpostag: True,
}
def read_conll(lines, lookup_lemmas=False):
"""
If no lemma is present and lookup_lemmas is True, consults WordNet by
calling get_lemma().
"""
result = []
for line in lines:
line = line.strip()
if line == '':
yield result
result = []
else:
token = ConllToken.from_line(line)
if lookup_lemmas:
token.lemma = get_lemma(token.form, token.postag)
result.append(token)
if result:
yield result
def build_target_dicts(targetdictFP,
unifreqFP,
dataFP='../scoring/cv.train.sentences.json'):
uniFreq = Counter()
targets = Counter()
with codecs.open(dataFP, 'r', 'utf-8') as inF:
for ln in inF:
sentence = json.loads(ln)
# unigrams
#print(sentence['tokens'],sentence['pos'], file=sys.stderr)
if len(sentence['tokens']) != len(sentence['pos']):
# sometimes the data is buggy and missing a POS tag :(
# stopgap: insert ? as the tag
# TODO: fix this in preprocessing, make an assert here
posmap = {stuff['start']: stuff for stuff in sentence['pos']}
sentence['pos'] = [
posmap.get(
i, {
'start': i,
'end': i + 1,
'name': '?',
'text': sentence['tokens'][i]
}) for i in range(len(sentence['tokens']))
]
tokenOffsets = set(
) # tokens of interest: either part of a target or present in the WSL
for wslentry in sentence['wsl']:
tokenOffsets |= {
i
for i in range(wslentry['start'], wslentry['end'])
}
# targets
for frame in sentence['frames']:
target_toks = {
i
for span in frame['target']['spans']
for i in range(span['start'], span['end'])
}
tokenOffsets |= target_toks
lemmas = [
get_lemma(entry['text'], entry['name']) + '_' +
entry['name'].upper()[0] for i in sorted(target_toks)
for entry in [sentence['pos'][i]]
]
targets[' '.join(lemmas)] += 1
# unigram counts
# ignore tokens tagged as proper nouns (consequence: if there are proper noun unigram targets,
# the target/unigram ratio can be greater than 1)
uniFreq.update(
get_lemma(entry['text'], entry['name']) + '_' +
entry['name'].upper()[0] for entry in sentence['pos']
if entry['start'] in tokenOffsets and not entry['name'].
startswith('NP') and not entry['name'].startswith('NNP'))
with codecs.open(unifreqFP, 'w', 'utf-8') as outF:
for w, n in uniFreq.items():
outF.write(w + '\t' + str(n) + '\n')
with codecs.open(targetdictFP, 'w', 'utf-8') as outF:
for ww, n in targets.items():
outF.write(ww + '\t' + str(n) + '\n')
