def smart_tag(tokens, tagger):
"""Tag a token stream using a pre-built tagger"""
tr1, tr2, tr3 = tee(iter_ngrams(tokens, 3), 3)
next(tr2)
next(tr3)
next(tr3)
for token, m3, r3 in tr3:
l1, m1, _ = next(tr1)
l2, __, r2 = next(tr2)
tag_ = tag(token)
if tag_ is not None:
yield token, tag_
continue
dist_ = {}
rtags = tagger['R'][(tag(l1), tag(m1))]
part_ = len(rtags)
if part_:
part_ = 200000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
rtags = tagger['M'][(tag(l2), tag(r2))]
part_ = len(rtags)
if part_:
part_ = 600000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
rtags = tagger['L'][(tag(m3), tag(r3))]
part_ = len(rtags)
if part_:
part_ = 200000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
if len(dist_) == 0:
yield token, None
continue
if 'Z' in dist_:
# damp the pronouns
rest = sum(dist_.values()) - dist_['Z']
prob = dist_['Z'] / ((rest + dist_['Z']) * 10) # 0.1 damping
dist_['Z'] = prob * rest / (1 - prob)
dist_ = dist_.items()
dist_.sort(key = key1, reverse = True)
yield token, dist_[0][0]
def smart_tag(tokens, tagger):
"""Tag a token stream using a pre-built tagger"""
tr1, tr2, tr3 = tee(iter_ngrams(tokens, 3), 3)
next(tr2)
next(tr3)
next(tr3)
for token, m3, r3 in tr3:
l1, m1, _ = next(tr1)
l2, __, r2 = next(tr2)
tag_ = tag(token)
if tag_ is not None:
yield token, tag_
continue
dist_ = {}
rtags = tagger['R'][(tag(l1), tag(m1))]
part_ = len(rtags)
if part_:
part_ = 200000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
rtags = tagger['M'][(tag(l2), tag(r2))]
part_ = len(rtags)
if part_:
part_ = 600000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
rtags = tagger['L'][(tag(m3), tag(r3))]
part_ = len(rtags)
if part_:
part_ = 200000 / part_
for t_ in rtags:
dist_[t_] = dist_.get(t_, 0) + part_
if len(dist_) == 0:
yield token, None
continue
if 'Z' in dist_:
# damp the pronouns
rest = sum(dist_.values()) - dist_['Z']
prob = dist_['Z'] / ((rest + dist_['Z']) * 10) # 0.1 damping
dist_['Z'] = prob * rest / (1 - prob)
dist_ = dist_.items()
dist_.sort(key = key1, reverse = True)
yield token, dist_[0][0]
def build_tagger(tokens):
"""Build tagger from basic analysis of a token stream"""
trigrams = iter_ngrams(tokens, 3)
tagger = {'L':defaultdict(list), 'M':defaultdict(list), 'R':defaultdict(list)}
builder_skip_tags = set(('NU', 'PU'))
for l, m, r in trigrams:
lt, mt, rt = map(tag, (l, m, r))
if reduce(or_, [tag_ is None for tag_ in (lt, mt, rt)]): continue
if lt not in builder_skip_tags:
tagger['L'][(mt, rt)].append(lt)
if mt not in builder_skip_tags:
tagger['M'][(lt, rt)].append(mt)
if rt not in builder_skip_tags:
tagger['R'][(lt, mt)].append(rt)
return tagger
def build_tagger(tokens):
"""Build tagger from basic analysis of a token stream"""
trigrams = iter_ngrams(tokens, 3)
tagger = {'L':defaultdict(list), 'M':defaultdict(list), 'R':defaultdict(list)}
builder_skip_tags = set(('NU', 'PU'))
for l, m, r in trigrams:
lt, mt, rt = map(tag, (l, m, r))
if reduce(or_, [tag_ is None for tag_ in (lt, mt, rt)]): continue
if lt not in builder_skip_tags:
tagger['L'][(mt, rt)].append(lt)
if mt not in builder_skip_tags:
tagger['M'][(lt, rt)].append(mt)
if rt not in builder_skip_tags:
tagger['R'][(lt, mt)].append(rt)
return tagger