def __init__(self, domain_port, domain_order,
gigaword_port, gigaword_order):
self.tok_ = RegexpTokenizer(r'\w+')
self.domain_lm_ = Client(domain_port, domain_order, True)
self.gigaword_lm_ = Client(gigaword_port, gigaword_order, True)
self.features = [u"LM_FEATS: domain avg lp",
u"LM_FEATS: gigaword avg lp"]
def __init__(self, domain_port, domain_order, gigaword_port,
gigaword_order):
self.tok_ = RegexpTokenizer(r'\w+')
self.domain_lm_ = Client(domain_port, domain_order, True)
self.gigaword_lm_ = Client(gigaword_port, gigaword_order, True)
self.features = [
u"LM_FEATS: domain avg lp", u"LM_FEATS: gigaword avg lp"
]
class LMProbExtractor(object):
def __init__(self, domain_port, domain_order, gigaword_port,
gigaword_order):
self.tok_ = RegexpTokenizer(r'\w+')
self.domain_lm_ = Client(domain_port, domain_order, True)
self.gigaword_lm_ = Client(gigaword_port, gigaword_order, True)
self.features = [
u"LM_FEATS: domain avg lp", u"LM_FEATS: gigaword avg lp"
]
def process_corenlp_strings(self, strings):
return [self.process_corenlp_string(string) for string in strings]
def process_corenlp_string(self, string):
dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob(string)
gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob(string)
return {
u"LM_FEATS: domain avg lp": dmn_avg_lp,
u"LM_FEATS: gigaword avg lp": gw_avg_lp
}
def process_article(self, si):
if u'article-clf' not in si.body.sentences:
return list()
lm_scores = []
for sentence in si.body.sentences[u'article-clf']:
bytes_string = ' '.join(token.token for token in sentence.tokens)
uni_string = bytes_string.decode(u'utf-8')
uni_string = uni_string.lower()
uni_tokens = self.tok_.tokenize(uni_string)
uni_string = u' '.join(uni_tokens)
bytes_string = uni_string.encode(u'utf-8')
dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob(
bytes_string)
gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob(
bytes_string)
lm_scores.append({
u"domain avg lp": dmn_avg_lp,
u"gigaword avg lp": gw_avg_lp
})
return lm_scores
class LMProbExtractor(object):
def __init__(self, domain_port, domain_order,
gigaword_port, gigaword_order):
self.tok_ = RegexpTokenizer(r'\w+')
self.domain_lm_ = Client(domain_port, domain_order, True)
self.gigaword_lm_ = Client(gigaword_port, gigaword_order, True)
self.features = [u"LM_FEATS: domain avg lp",
u"LM_FEATS: gigaword avg lp"]
def process_corenlp_strings(self, strings):
return [self.process_corenlp_string(string) for string in strings]
def process_corenlp_string(self, string):
dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob(string)
gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob(string)
return {u"LM_FEATS: domain avg lp": dmn_avg_lp,
u"LM_FEATS: gigaword avg lp": gw_avg_lp}
def process_article(self, si):
if u'article-clf' not in si.body.sentences:
return list()
lm_scores = []
for sentence in si.body.sentences[u'article-clf']:
bytes_string = ' '.join(token.token for token in sentence.tokens)
uni_string = bytes_string.decode(u'utf-8')
uni_string = uni_string.lower()
uni_tokens = self.tok_.tokenize(uni_string)
uni_string = u' '.join(uni_tokens)
bytes_string = uni_string.encode(u'utf-8')
dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob(
bytes_string)
gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob(
bytes_string)
lm_scores.append(
{u"domain avg lp": dmn_avg_lp,
u"gigaword avg lp": gw_avg_lp})
return lm_scores
