add_verbs.py
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#-*- coding:utf-8 -*-
import codecs
import re
from django.core.management.base import BaseCommand
from lxml import etree
from dictionary.models import Entry, Lemma, Lemma_Status, POS, Vocabulary
VERBS_IN_DICT = 2000
POLANSKI_PATH = 'data/dictionary.xml'
B_PATH = 'data/B_frames/B_cz_frames.txt'
PATH_300M = 'data/susp-1.1-verbs-300M-counts.txt'
NEW_VERBS_PATH = 'data/new_verbs_140213.txt'
NWALENTY_PATH = 'data/checked-nwalenty.txt'
# dodawanie nowych haseł, skryptami:
# add_verbs
# load_frequency
# !!! po wprowadzeniu haseł sprawdzić czy nie ma niedodanych multirelacji postaci \).*\( !!!!!!!!
ADJS_RELATIONS_PATH = 'data/nverbs/relations/merged_adjs+verb-freq.txt'
NOUNS_RELATIONS_PATH = 'data/nverbs/relations/nouns+verb-freq.txt'
class Command(BaseCommand):
args = 'none'
help = """
Add verbs from given freqency list. Script checks if verb
is not already included in Slowal tool database.
"""
def handle(self, **options):
#add_verbs_intersec_freq()
#get_new_verbs()
#add_verbs(NEW_VERBS_PATH, 'data/added_verbs_20140701_pol_i_tajny.txt')
#add_verbs(PATH_300M, 'data/added_verbs_20140701.txt')
verbs = add_verbs('data/verbs/verbs2consider-1M-300M-all.txt',
'data/verbs/added_verbs_20170303.txt')
# add_relations_by_verb_entries(verbs, ADJS_RELATIONS_PATH, 'adj')
# add_relations_by_verb_entries(verbs, NOUNS_RELATIONS_PATH, 'noun')
def add_verbs(verbs_path, added_path):
added_verbs = []
added_file = codecs.open(added_path, 'wt', 'utf-8')
dict_basename = 'clarin2_verbs1'
dict = 0
new_last_dict = 1
verbs_per_dict = VERBS_IN_DICT
min_freq = 0
with codecs.open(verbs_path, 'rt', 'utf8') as infile:
if dict == 0:
new_voc = Vocabulary(name=dict_basename)
new_voc.save()
else:
new_voc = Vocabulary(name=dict_basename+str(dict))
new_voc.save()
initial_status = Lemma_Status.objects.order_by('priority')[0]
for line in infile:
line = line.strip()
if line.startswith('*'):
continue
print line
line_ls = line.split()
word = line_ls[0].strip()
freq_1M = int(line_ls[1].strip())
freq_300M = int(line_ls[2].strip())
lemmas = Lemma.objects.filter(entry = word)
if lemmas.count() == 0 and freq_300M >= min_freq:
if verbs_per_dict == 0:
verbs_per_dict = VERBS_IN_DICT
dict += 1
if dict > new_last_dict:
break
else:
new_voc = Vocabulary(name=dict_basename+str(dict))
new_voc.save()
entry_obj = get_verb_entry(word)
new_lemma = Lemma(entry_obj=entry_obj,
entry=word, vocabulary=new_voc,
status=initial_status, old=False,
frequency_300M=freq_300M, frequency_1M=freq_1M)
new_lemma.save()
verbs_per_dict -= 1
added_file.write('%s\t%s\t%d\n' % (dict_basename+str(dict), word, freq_300M))
added_verbs.append(word)
added_file.close()
return added_verbs
def get_verb_entry(verb):
try:
entry = Entry.objects.get(name=verb, pos__tag='verb')
except Entry.DoesNotExist:
try:
entry = Entry.objects.get(name=verb, pos__tag='unk')
verb_pos = POS.objects.get(tag='verb')
entry.pos = verb_pos
entry.save()
except Entry.DoesNotExist:
verb_pos = POS.objects.get(tag='verb')
entry = Entry(name=verb, pos=verb_pos)
entry.save()
return entry
def add_relations_by_verb_entries(entries, relations_path, pos_tag):
print 'Adding relations!'
pos = POS.objects.get(tag=pos_tag)
try:
freq_file = codecs.open(relations_path, "rt", 'utf-8')
for line in freq_file:
line_ls = line.split()
verb = line_ls[3].lstrip('(').strip()
try:
nverb = line_ls[0].strip()
if verb in entries:
verb_obj = Lemma.objects.get(old=False, entry=verb, entry_obj__pos__tag='verb')
nverb_obj = Lemma.objects.get(old=False, entry=nverb, entry_obj__pos=pos)
nverb_entry = nverb_obj.entry_obj
verb_entry = verb_obj.entry_obj
verb_entry.rel_entries.add(nverb_entry)
nverb_entry.rel_entries.add(verb_entry)
print line
except Lemma.DoesNotExist:
pass
finally:
freq_file.close()
def get_polanski_verbs(inpath):
verbs = []
tree = etree.parse(inpath)
words = tree.xpath("//*[local-name() = 'orth']")
for word in words:
verb = word.text.replace(u'się', '').strip()
if verb not in verbs:
verbs.append(verb)
print verb
return verbs
def get_B_verbs(inpath):
verbs = []
try:
f = codecs.open(inpath, "rt", 'utf-8')
for line in f:
line_pattern = re.compile(ur"^([^\d]+)[\d]+(.*)$")
m = line_pattern.match(line)
if not m:
print '!!!!!!!!!!!!!!!!match error!!!!!!!!!!!!!!!!!!!!!!!'
if m:
lemma_str = m.group(1).strip()
lemma_ls = lemma_str.split()
line = line.strip()
if not lemma_ls[0] in verbs:
verbs.append(lemma_ls[0])
finally:
f.close()
return verbs
def load_B_lemmas(inpath, voc_name):
print 'Loading %s dict.' % (voc_name)
try:
f = codecs.open(inpath, "rt", 'utf-8')
voc_obj, xx = Vocabulary.objects.get_or_create(name=voc_name)
initial_status = Lemma_Status.objects.get(status=u'do obróbki')
for line in f:
line_ls = line.split()
entry = line_ls[1].strip()
try:
Lemma.objects.get(old=False, entry=entry)
except Lemma.DoesNotExist:
lemma_obj, created = Lemma.objects.get_or_create(old=False,
entry=entry,
vocabulary=voc_obj,
status=initial_status)
if created:
voc_obj.lemmas.add(lemma_obj)
finally:
f.close()
def compare_to_300M(pol_verbs, b_verbs, path_300M, outpath, nwalenty_path):
try:
pol_verbs_to_check = []
file_300M = codecs.open(path_300M, "rt", 'utf-8')
outfile = codecs.open(outpath, 'wt', 'utf-8')
nwalenty_file = codecs.open(nwalenty_path, 'wt', 'utf-8')
for line in file_300M:
print line.strip()
if line.strip().startswith('*'):
continue
line_ls = line.split()
entry = line_ls[0].strip()
if entry in pol_verbs and not entry in b_verbs:
pol_verbs_to_check.append(entry)
if Lemma.objects.filter(old=False, entry=entry).exists():
continue
if entry in b_verbs or entry in pol_verbs:
outfile.write(line)
else:
nwalenty_file.write(line)
finally:
file_300M.close()
outfile.close()
def get_new_verbs():
pol_verbs = get_polanski_verbs(POLANSKI_PATH)
b_verbs = get_B_verbs(B_PATH)
compare_to_300M(pol_verbs, b_verbs, PATH_300M, NEW_VERBS_PATH, NWALENTY_PATH)
def add_verbs_intersec_freq():
verbs_path = 'data/polanski_verbs_freq_list.txt'
added_path = 'data/added_verbs_clarin6.txt'
added_file = codecs.open(added_path, 'wt', 'utf-8')
dict_basename = 'clarin'
dict = 6
new_last_dict = 10
verbs_per_dict = VERBS_IN_DICT
with codecs.open(verbs_path,'rt', 'utf8') as infile:
new_voc = Vocabulary(name=dict_basename+str(dict))
new_voc.save()
initial_status = Lemma_Status.objects.order_by('priority')[0]
for line in infile:
line = line.strip()
ngram_pattern = re.compile(ur'^[\s]*([\d]+)[\s]*([^\s]+).*$')
m = ngram_pattern.match(line)
if m:
freq = int(m.group(1).strip())
word = m.group(2).strip()
lemmas = Lemma.objects.filter(entry = word)
if lemmas.count() == 0:
if verbs_per_dict == 0:
verbs_per_dict = VERBS_IN_DICT
dict += 1
if dict > new_last_dict:
break
else:
new_voc = Vocabulary(name=dict_basename+str(dict))
new_voc.save()
new_lemma = Lemma(entry=word, vocabulary=new_voc,
status=initial_status, old=False)
new_lemma.save()
verbs_per_dict -= 1
added_file.write(dict_basename+str(dict) + ' ' + word +
' ' + str(freq) + '\n')
added_file.close()