Moved to a different approach with estnltk library

estnltk_preprocessing.py

+from estnltk import Text
+from util import save_dict, load_dict, save_csv, read_csv
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def map_verbs_with_sentences():
+	verbs = {}
+	articles = Text(open('articles.txt', encoding='utf-8').read())
+	articles = articles.replace('\xa0', ' ')
+
+	for sentence in articles.sentence_texts:
+		words = Text(sentence).get.word_texts.lemmas.postags.as_dataframe
+		for verb in words[words['postags'] == 'V']['lemmas']:
+			if verb != 'ei':
+				if verb in verbs:
+					verbs[verb].append(sentence)
+				else:
+					verbs[verb] = [sentence]
+		save_dict(verbs, 'verbs_dict')
+		#print(sentence)
+
+def verbs_dict_to_df():
+	print('Loading verbs...')
+	verbs = load_dict('verbs_dict')
+	print('Finished loading verbs')
+	rows_list = []
+	total_verbs = len(verbs)
+	i = 0
+	for verb in verbs:
+		print('%i/%i %s' % (i, total_verbs, verb))
+		for sentence in verbs[verb]:
+			sentence = Text(sentence.replace('\n', '').replace('~', ''))
+
+			words = Text(sentence).get.word_texts.lemmas.postags.forms.as_dataframe
+			noun_likes = words[(words['postags'] == 'P') | (words['postags'] == 'S') | (words['postags'] == 'H') | (words['postags'] == 'A')
+			 | (words['postags'] == 'O') | (words['postags'] == 'N') | (words['postags'] == 'U') | (words['postags'] == 'Y')]
+			for index, noun_like in noun_likes.iterrows():
+				for verb_occurence_index in words.index[words['lemmas'] == verb]:
+					dict = {'verb': verb, 'noun_like': noun_like['lemmas'], 'verb_form': words.iloc[verb_occurence_index]['forms'], 'noun_like_form': noun_like['forms'], 'noun_like_pos': noun_like['postags'], 'sentence': sentence, 'distance': abs(verb_occurence_index - index) }
+					rows_list.append(dict)
+		i += 1
+	return pd.DataFrame.from_dict(rows_list)
+
+def clean_dataframe():
+	df = read_csv('verbs_with_noun_likes.csv', sep='~')
+	df.columns = ['distance', 'noun_like', 'noun_like_form', 'noun_like_pos', 'sentence', 'verb', 'verbs_form']
+	df = df[pd.notnull(df['noun_like_form'])] # remove examples with null forms
+	df = df[~df['noun_like_form'].str.contains('\|')] # remove example with several forms
+	df = df[df['noun_like_form'] != '?'] # remove examples with unknown forms
+	save_csv(df, 'cleaned_dataframe.csv', sep='~')
+	print(df[df['distance'] > 100]['sentence'])
+	plt.scatter(df['distance'], df['noun_like_form'], alpha=0.2, c='k')
+	plt.show()
+
+clean_dataframe()
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estnltk_random_verbs_to_label.py

+from estnltk import Text, TextCleaner, ESTONIAN
+import random
+from util import load_dict, save_dict
+
+def save_random_verbs():
+	ESTONIAN = ESTONIAN + '«»„ˮ“€’.…'
+	text_cleaner = TextCleaner(ESTONIAN)
+	verbs = load_dict('verbs_dict')
+	verbs_to_label = {}
+	for i in range(100):
+		random_verb = list(random.choice(list(verbs.items())))
+		verbs_to_label[random_verb[0]] = random.choice(random_verb[1])
+	print(verbs_to_label)
+	save_dict(verbs_to_label, 'verbs_with_labels')
+
+def load_random_verbs():
+	verbs = load_dict('verbs_with_labels')
+	for verb in verbs:
+		sentence = verbs[verb]
+		print('%s: \'%s\'' % (verb, sentence))
+		sentence = Text(sentence).get.word_texts.lemmas.postags.as_dataframe
+		print(sentence)
+
+
+load_random_verbs()
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preprocessing.py

@@ -2,37 +2,39 @@ from util import save_csv, get_preprocessed_verbs, get_articles, write_string_to
 from tqdm import tqdm
 import io, re, ast, csv
 
-def get_verbs_with_not_empty_occurences():
+def get_verbs_with_cleaned_occurences():
 	csv.field_size_limit(100000000)
 	verbs = get_verbs_with_occurences()
 	verbs = verbs.loc[verbs[9] != '[]'][[0, 9]]
 	verbs.index = range(len(verbs))
+	verbs[9] = verbs[9].str.replace('\\xa0', ' ', regex=False)
+	print(verbs[9])
 	save_csv(verbs, 'cleaned_verbs_with_occurences.csv')
-	print(verbs)
 
 def extract_verbs_occurences_from_articles(verbs, articles):
 	verbs['occurences'] = ''
-	for i in tqdm(range(1473, len(verbs))):
+	verbs['forms'] = ''
+	for i in tqdm(range(len(verbs))):
 		spaced_verb = ' ' + verbs[8][i]
 		occurences = list(set([sentence + '.' for sentence in articles.split('.') if spaced_verb in sentence]))
-		verbs['occurences'][i] = filter_wrong_occurences(verbs.iloc[i], occurences)
+		cleaned_occurences = filter_wrong_occurences(verbs.iloc[i], occurences)
+		verbs['occurences'][i], verbs['forms'][i] = cleaned_occurences[0], cleaned_occurences[1]
 		save_csv(verbs, "with_approximate_occurences_1473.csv")
 
 def filter_wrong_occurences(verb, occurences):
 	all_forms = get_all_forms(verb)
-	verified_occurences = []
+	verified_occurences = [[],[]]
+
 	for occurence in occurences:
 		found = False
 		for form in all_forms:
 			if form in occurence:
 				pattern = re.compile('.*'+form+'(\W.*)*$')
 				if pattern.match(occurence):
-					verified_occurences.append(occurence)
+					verified_occurences[0].append(occurence)
+					verified_occurences[1].append(form)
 					found = True
 					break
-		# if not found:
-		# 	not_found = ('%s was not found in \"%s\"\n' % (verb[0], occurence))
-		# 	write_string_to_file(not_found, 'not_found.txt', 'a')
 	return verified_occurences
 
 
@@ -69,8 +71,8 @@ def forms_from_dud(root):
 	endings = ['ud', 'av', 'avat', 'agu', 'i', 'a']
 	return forms(root, endings)
 
-# verbs = get_preprocessed_verbs()
-# articles = get_articles().lower()
-# extract_verbs_occurences_from_articles(verbs, articles)
+verbs = get_preprocessed_verbs()
+articles = get_articles().lower()
+extract_verbs_occurences_from_articles(verbs, articles)
 
-get_verbs_with_not_empty_occurences()
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+# get_verbs_with_cleaned_occurences()
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util.py

 import pandas as pd
-import urllib, io
+import urllib, io, pickle
 from bs4 import BeautifulSoup
 
 def get_soup(url):
@@ -20,8 +20,8 @@ def write_string_to_file(string, path, mode):
 	with io.open(path, mode, encoding='utf-8') as file:
 		file.write(string)
 
-def save_csv(df, path):
-	df.to_csv(path, index=False, header=False)
+def save_csv(df, path, sep=','):
+	df.to_csv(path, index=False, header=False, sep=sep)
 
 def read_csv(path, sep=',', header=None):
 	df = pd.read_csv(path, sep=sep, encoding='utf8', header=header, engine='python')
@@ -40,3 +40,11 @@ def get_postimees_urls():
 
 def get_verbs_with_occurences():
 	return read_csv("with_approximate_occurences_all.csv")
+
+def load_dict(name):
+    with open(name + '.pkl', 'rb') as f:
+        return pickle.load(f)
+
+def save_dict(obj, name):
+    with open(name + '.pkl', 'wb') as f:
+        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
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