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raw_text_processing.py
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raw_text_processing.py
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# -*- coding: utf-8 -*-
###########################################################################
# Pre-processing raw text
# Date: November 2017
###########################################################################
import os
import re
import nltk # Natural Language toolkit
from nltk.tokenize import sent_tokenize, word_tokenize # form tokens from words/sentences
import matplotlib
matplotlib.use('Agg')
import string
import csv
from datetime import datetime
from collections import namedtuple, Counter
import itertools
from itertools import imap, permutations # set up namedtuple
from collections import defaultdict # create dictionary with empty list for values
import matplotlib.pyplot as plt # graphs
import networkx as nx
import numpy as np
########################################################################
## READING AND TOKENIZATION OF RAW TEXT (PRE-PROCESSING)
#TODO: move to a seperate file (up to readFile)
basic_pronouns = "I Me You She He Him It We Us They Them Myself Yourself Himself Herself Itself Themselves My your Her Its Our Their His"
possessive_pronouns = "mine yours his hers ours theirs my"
reflexive_pronouns = "myself yourself himself herself itself oneself ourselves yourselves themselves you've"
relative_pronouns = "that whic who whose whom where when"
neutral_pronouns = ['I', 'Me', 'You', 'It', 'We', 'Us', 'They', 'Them', 'Myself', 'Mine',
'Yourself', 'Itself', 'Themselves', 'My', 'Your', 'Its', 'Our', 'Their',
"One", "Ourselves", 'Yours']
female_pronouns = ['Her', 'Hers', 'Herself', "She", 'Herself']
male_pronouns = ['He', 'Him', 'His', 'Himself']
male_honorific_titles = ['M', 'Mr', 'Sir', 'Lord', 'Master', 'Gentleman',
'Sire', "Esq", "Father", "Brother", "Rev", "Reverend",
"Fr", "Pr", "Paster", "Br", "His", "Rabbi", "Imam",
"Sri", "Thiru", "Raj", "Son", "Monsieur", "M", "Baron",
"Prince", "King", "Emperor", "Grand Prince", "Grand Duke",
"Duke", "Sovereign Prince", "Count", "Viscount", "Crown Prince",
'Gentlemen', 'Uncle', 'Widower', 'Don', "Mistah", "Commodore",
"Grandfather", "Mister", "Brother-in-Law", "Mester", "Comrade", "Lordship"]
female_honorific_titles = ['Mrs', 'Ms', 'Miss', 'Lady', 'Mistress',
'Madam', "Ma'am", "Dame", "Mother", "Sister",
"Sr", "Her", "Kum", "Smt", "Ayah", "Daughter",
"Madame", "Mme", 'Madame', "Mademoiselle", "Mlle", "Baroness",
"Maid", "Empress", "Queen", "Archduchess", "Grand Princess",
"Princess", "Duchess", "Sovereign Princess", "Countess",
"Gentlewoman", 'Aunt', 'Widow', 'Doha', 'Comtesse', 'Baronne',
"Grandmother", "Sister-in-Law", "Missus"]
ignore_neutral_titles = ['Dr', 'Doctor', 'Captain', 'Capt',
'Professor', 'Prof', 'Hon', 'Honor', "Excellency",
"Honourable", "Honorable", "Chancellor", "Vice-Chancellor",
"President", "Vice-President", "Senator", "Prime", "Minster",
"Principal", "Warden", "Dean", "Regent", "Rector",
"Director", "Mayor", "Judge", "Cousin", 'Archbishop',
'General', 'Secretary', 'St', 'Saint', 'San', 'Assistant', "Director",
"The Right Honorable", "The Right Honourable", "Highness", "Cuz", "Poor",
"Silly", "Old"]
all_honorific_titles = male_honorific_titles + female_honorific_titles + ignore_neutral_titles
male_equ_titles = [['M', 'Mr', 'Mister', 'Mistah', 'Mester', 'Monsieur']]
female_equ_titles = [['Ms', 'Miss', 'Missus', 'Mademoiselle', 'Mlle'],
['Madam', "Ma'am", "Madame", "Mme"]]
neutral_equ_titles = [['Dr', 'Doctor'], ['Capt', 'Captain'],
['Professor', 'Prof'], ['St', 'Saint'],
["Cousin", "Cuz"]]
all_equal_titles = male_equ_titles + female_equ_titles + neutral_equ_titles
all_equal_titles = [item for sublist in all_equal_titles for item in sublist] # flat list of all titles for checking
potential_names_with_equal_titles = []
connecting_words = ["of", "the", "De", "de", "La", "la", 'al', 'y', 'Le', 'Las']
# WORDS TO IGNORE (parser has mislabeled)
words_to_ignore = ["Dear", "Chapter", "Volume", "Man", "O", "Anon", "Ought",
"Thou", "Thither", "Yo", "Till", "Ay", "Dearest", "Dearer", "Though",
"Hitherto", "Ahoy", "Alas", "Yo", "Chapter", "Again", "'D", "One", "'T",
"If", "thy", "Thy", "Thee", "Suppose", "There", "'There", "No-One", "Happily",
"Good-Night", "Good-Morning", 'To-Day', 'To-Morrow', "Compare", "Tis", "Good-Will",
'To-day', 'To-morrow', 'To-Night', 'Thine', 'Or', "D'You", "O'Er", "Aye", "Men"
"Ill", "Behold", "Beheld", "Nay", "Shall", "So-And-So", "Making-Up", "Ajar",
"Show", "Interpreting", "Then", "No", "Alright", "Tell", "Thereupon", "Yes",
"Abandon", "'But", "But", "'Twas", "Knelt", "Thou", "True", "False",
"Overhead", "Ware", "Fortnight", "Good-looking", "Something", "Grants", "Rescue",
"Head", "'Poor", "Tha'", "Tha'Rt", "Eh", "Whither", "Ah", "Sends",
"Silly", "Methought", "Come", "Dost", "Wilt", "Wherefore", "Doth", "Betwixt",
"Dat", "Midsummer", "Withal", "Thyself", "Shoots", "Came", "Sayeth",
"Aids", "Wilt", "Thou", "Whereupon", "Spake", "Poor", "Describe", "Opposite",
"Found", "Fish", "Woke", "Dim", "Alone", "Gwine", "`The", "O'Er", "Into", "mid-word"] # ignores noun instances of these word by themselves
#words_to_ignore += ["".join(a) for a in permutations(['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X'], 2)]
#words_to_ignore += ["".join(a) for a in ['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X', 'XV']]
numbers_as_words = {1: 'One', 2: 'Two', 3: 'Three', 4: 'Four', 5: 'Five',
6: 'Six', 7: 'Seven', 8: 'Eight', 9: 'Nine', 10: 'Ten',
11: 'Eleven', 12: 'Twelve', 13: 'Thirteen', 14: 'Fourteen',
15: 'Fifteen', 16: 'Sixteen', 17: 'Seventeen', 18: 'Eighteen',
19: 'Nineteen', 20: 'Twenty', 30: 'Thirty', 40: 'Forty',
50: 'Fifty', 60: 'Sixty', 70: 'Seventy', 80: 'Eighty',
90: 'Ninety', 0: 'Zero'}
total_numbers = numbers_as_words.values()
words_to_ignore += ["Chapter {0}".format("".join(a)) for a in []]
words_to_ignore += ["Chapter {0}".format("".join(a)) for a in permutations(['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X'], 2)]
words_to_ignore += ["Chapter {0}".format("".join(a)) for a in ['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X']]
words_to_ignore += ["Chapter {0}".format("".join(a)) for a in permutations(['1','2','3','4','5','6','7','8','9','10'], 2)]
words_to_ignore += ["Chapter {0}".format("".join(a)) for a in ['1','2','3','4','5','6','7','8','9','10']]
words_to_ignore += ["CHAPTER {0}".format("".join(a)) for a in permutations(['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X'], 2)]
words_to_ignore += ["CHAPTER {0}".format("".join(a)) for a in ['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X']]
words_to_ignore += ["Volume {0}".format("".join(a)) for a in permutations(['1','2','3','4','5','6','7','8','9','10'], 2)]
words_to_ignore += ["Volume {0}".format("".join(a)) for a in ['1','2','3','4','5','6','7','8','9','10']]
words_to_ignore += ["Volume {0}".format("".join(a)) for a in permutations(['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X'], 2)]
words_to_ignore += ["Volume {0}".format("".join(a)) for a in ['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X']]
words_to_ignore += ["VOLUME {0}".format("".join(a)) for a in permutations(['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X'], 2)]
words_to_ignore += ["VOLUME {0}".format("".join(a)) for a in ['I', 'II','III', 'IV', 'VI', 'XX', 'V', 'X']]
words_to_ignore += ["VOLUME {0}".format("".join(a)) for a in permutations(['1','2','3','4','5','6','7','8','9','10'], 2)]
words_to_ignore += ["VOLUME {0}".format("".join(a)) for a in ['1','2','3','4','5','6','7','8','9','10']]
def readFile(filename):
file_remove_extra = []
with open(filename, "r") as given_file:
string_words = given_file.read()
string_words = string_words.replace("\r\n\r\n", ".") # create sentences for chapter titles
string_words = string_words.replace("\n", " ")
string_words = string_words.replace("--", ", ")
string_words = string_words.replace("; ", ", ")
string_words = string_words.replace("*", "")
string_words = string_words.replace("_", "")
string_words = string_words.replace("!”", "!”.") # “Ah-h-h!” “How true!” “Amazing, amazing!” into sub-sentences (twain)
string_words = string_words.replace(".”", "”.") # splits the .". into two sentences at the end of a quote
string_words = string_words.replace("?”", "?”.") # splits ? into a sub-sentence
string_words = string_words.replace("—”", "”.") # splits dash into a sub-sentence
string_words = string_words.replace("'I", "' I") # fix puncutation where I
string_words = string_words.replace("'It", "' It")
string_words = string_words.replace("'we", "' we")
string_words = string_words.replace("'We", "' We")
string_words = string_words.replace("Mr.", "Mr") # period created breaks when spliting
string_words = string_words.replace("Ms.", "Ms")
string_words = string_words.replace("Mrs.", "Mrs")
string_words = string_words.replace("Dr.", "Dr")
string_words = string_words.replace("St.", "St")
# replace utf-8 elements
string_words = string_words.replace("’", "\'") # replace signal quotes
string_words = string_words.replace("“", "~\"") # isolate dialouge double quotes
string_words = string_words.replace("” ", "\"~")
string_words = re.sub(r'[\x90-\xff]', ' ', string_words, flags=re.IGNORECASE) # remove unicode (dash)
string_words = re.sub(r'[\x80-\xff]', '', string_words, flags=re.IGNORECASE) # remove unicode
file_remove_extra = string_words.split(' ')
file_remove_extra = filter(None, file_remove_extra) # remove empty strings from list
return file_remove_extra
def isDialogue(sentence):
# return true/false if the value is a quote
return '"' in sentence
def tokenizeSentence(string_sentence):
'''EXAMPLE
{60: 'After rather a long silence, the commander resumed the conversation.'}
'''
tokens_sentence_dict = {} # returns dict with {token location in text #: sentence}
tokens_sent = string_sentence.split('.')
index = 0
for t in range(len(tokens_sent)):
sent = tokens_sent[t].strip() # remove excess whitespace
for dia in sent.split('~'):
if dia != '': # store dialouge with its double quotes for identification
tokens_sentence_dict[index] = dia # {4: '"Oh, why can\'t you remain like this for ever!"'}
index += 1
t += 1
#print(tokens_sentence_dict)
return tokens_sentence_dict
def partsOfSpeech(token_dict):
'''EXAMPLE
60: ('After rather a long silence, the commander resumed the conversation.',
[('After', 'IN'), ('rather', 'RB'), ('a', 'DT'), ('long', 'JJ'), ('silence', 'NN'),
(',', ','), ('the', 'DT'), ('commander', 'NN'), ('resumed', 'VBD'), ('the', 'DT'),
('conversation', 'NN'), ('.', '.')])}
'''
from subprocess import check_output
import progressbar as pb
widgets = ['Running POS tagger: ', pb.Percentage(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' ', pb.ETA()]
timer = pb.ProgressBar(widgets=widgets, maxval=len(token_dict)).start()
for i in range(len(token_dict)):
timer.update(i)
no_punc = token_dict[i].translate(None, string.punctuation) # remove puncuation from part of speech tagging
pos_tagged = check_output(["./3_run_text.sh", token_dict[i]])
if "docker not running, required to run syntaxnet" not in pos_tagged:
pos_tagged = process_POS_conll(pos_tagged) # process conll output from shell
token_dict[i] = (token_dict[i], pos_tagged) # adds part of speech tag for each word in the sentence
else:
print("\n\tWARNING: docker not running, cannot run syntaxnet for POS, exiting")
exit()
timer.finish()
return token_dict
def process_POS_conll(conll_output):
'''
['1', 'At', '_', 'ADP', 'IN', '_', '13', 'prep', '_', '_']
['2', 'the', '_', 'DET', 'DT', '_', '3', 'det', '_', '_']
['3', 'period', '_', 'NOUN', 'NN', '_', '1', 'pobj', '_', '_']
['4', 'when', '_', 'ADV', 'WRB', '_', '7', 'advmod', '_', '_']
['5', 'these', '_', 'DET', 'DT', '_', '6', 'det', '_', '_']
['6', 'events', '_', 'NOUN', 'NNS', '_', '7', 'nsubj', '_', '_']
['7', 'took', '_', 'VERB', 'VBD', '_', '3', 'rcmod', '_', '_']
['8', 'place', '_', 'NOUN', 'NN', '_', '7', 'dobj', '_', '_']
'''
pos_processed = conll_output
#print(pos_processed)
start_data = 0
pos_processed = re.sub("\t", ",", pos_processed.strip())
pos_processed = re.sub(",", " ", pos_processed.strip())
pos_processed = pos_processed.splitlines()
for i in range(len(pos_processed)):
pos_processed[i] = pos_processed[i].split(" ")
#print(pos_processed[i])
return pos_processed
########################################################################
## GROUP PROPER NOUNS ENTITIES
def findProperNamedEntity(pos_dict):
# returns {sentence index: [list of all proper nouns grouped]
# {0: ["Scarlett O'Hara", 'Tarleton'], 1: ['Coast']}
pos_type_lst = []
# TODO: EXPAND PROPER NOUNS FOR COMMON WORDS AROUND WORD
previous_nnp_index = 0
for row, pos_named in pos_dict.iteritems():
if "NNP" in pos_named.XPOSTAG: #"NN" in pos_named.XPOSTAG or "POS" in pos_named.XPOSTAG or "IN" in pos_named.XPOSTAG or "DT" in pos_named.XPOSTAG:
pos_type_lst.append((int(pos_named.SENTENCE_INDEX), int(pos_named.ID), pos_named.FORM, int(pos_named.SENTENCE_LENGTH), pos_named.XPOSTAG))
previous_nnp_index = int(pos_named.ID)
if pos_named.FORM in connecting_words:
pos_type_lst.append((int(pos_named.SENTENCE_INDEX), int(pos_named.ID), pos_named.FORM, int(pos_named.SENTENCE_LENGTH), pos_named.XPOSTAG))
previous_nnp_index = int(pos_named.ID)
#if "the" in pos_named.FORM or "The" in pos_named.FORM:
# if previous_nnp_index < int(pos_named.ID): # only store of if it is part of an existing sentence
# pos_type_lst.append((int(pos_named.SENTENCE_INDEX), int(pos_named.ID), pos_named.FORM, int(pos_named.SENTENCE_LENGTH), pos_named.XPOSTAG))
#if "of" in pos_named.FORM:
# if previous_nnp_index < int(pos_named.ID): # only store of if it is part of an existing sentence
# pos_type_lst.append((int(pos_named.SENTENCE_INDEX), int(pos_named.ID), pos_named.FORM, int(pos_named.SENTENCE_LENGTH), pos_named.XPOSTAG))
total_sentence_indices = list(set([i[0] for i in pos_type_lst]))
sub_sentences = []
for index in total_sentence_indices:
# create sub sentences for each sentence [[0], [1])
sub_sentences.append([x for x in pos_type_lst if x[0] == index])
#print("\nsub_sentence={0}\n".format(sub_sentences))
from operator import itemgetter # find sequences of consecutive values
import itertools
grouped_nouns = {}
names_lst = []
sentence_index = []
for sentence in sub_sentences:
noun_index = [s_index[1] for s_index in sentence] # noun location in a sentence (index)
#print(sentence, noun_index)
consec_lst = []
for k, g in itertools.groupby(enumerate(noun_index), lambda x: x[1]-x[0]):
consec_order = list(map(itemgetter(1), g))
if len(consec_order) > 0: # if there is more than one noun in an order for a sentence
consec_lst.append(consec_order)
#consec_lst = [item for items in consec_lst for item in items]
for c_l in consec_lst:
g_name = [x for x in sentence if x[1] in c_l]
nnp_in_sentence = False
for i, v in enumerate(g_name):
nnp_in_sentence = "NNP" in v
if nnp_in_sentence: # if the nnp exist in the sub-list, exit and save
break
if nnp_in_sentence:
#print(c_l)
#print([x[2] for x in sentence if x[1] in c_l])
#print(" ".join([x[2] for x in sentence if x[1] in c_l]))
start_with_connecting_ignore = [x[2] for x in sentence if x[1] in c_l][0] in connecting_words
end_with_connecting_ignore = [x[2] for x in sentence if x[1] in c_l][-1] in connecting_words
if start_with_connecting_ignore or end_with_connecting_ignore:
# if the gne starts with a connecting word, ignore the connecting name: 'of Divine Providence' -> 'Divine Providence'
new_start_index = 0
for first_words in [x[2] for x in sentence if x[1] in c_l]:
if first_words not in connecting_words:
break # if it doesn't start with a connecting word, ignore
else:
new_start_index += 1
#print([x[2] for x in sentence if x[1] in c_l][new_start_index:])
#print(" ".join([x[2] for x in sentence if x[1] in c_l][new_start_index:]))
new_end_index = len([x[2] for x in sentence if x[1] in c_l]) # last element after it is been updated
for last_words in reversed([x[2] for x in sentence if x[1] in c_l]):
# if the gne ends with a connecting word, ignore the connecting name: 'Tom of the' -> 'Tom'
if last_words not in connecting_words:
break
else:
new_end_index -= 1
#if new_end_index < len([x[2] for x in sentence if x[1] in c_l]):
# print("original: {0}".format(" ".join([x[2] for x in sentence if x[1] in c_l])))
# print("update: {0}\n".format(" ".join([x[2] for x in sentence if x[1] in c_l][new_start_index:new_end_index])))
# print(new_start_index, new_end_index)
if (new_end_index != 0) and (new_start_index != len([x[2] for x in sentence if x[1] in c_l])): # if the entire gne wasn't connecting words
names_lst.append(" ".join([x[2] for x in sentence if x[1] in c_l][new_start_index:new_end_index]))
sentence_index.append(list(set([x[0] for x in sentence if x[1] in c_l][new_start_index:new_end_index]))[0])
else:
names_lst.append(" ".join([x[2] for x in sentence if x[1] in c_l]))
sentence_index.append(list(set([x[0] for x in sentence if x[1] in c_l]))[0])
dic_tmp = zip(sentence_index, names_lst)
grouped_nouns = defaultdict(list)
for s, n in dic_tmp:
grouped_nouns[s].append(n)
return dict(grouped_nouns)
def commonSurrouding(grouped_nouns_dict):
# find the most common preceding words to append
pass
def groupSimilarEntities(grouped_nouns_dict):
# filter out enities that only appear once and re-organize
'''
[['America'], ['Aronnax', 'Pierre', 'Pierre Aronnax'],
['Captain Farragut', 'Captain', 'Farragut'], ['Conseil'],
['English'], ['Europe'], ['French'], ['Gentlemen'], ['God'],
['Land', 'Mr Ned Land', 'Ned', 'Ned Land'], ['Latin'],
['Lincoln', 'Abraham', 'Abraham Lincoln'], ['Museum'],
['Natural'], ['OEdiphus'], ['Pacific'], ['Paris'], ['Professor'],
['Sir'], ['Sphinx'], ['United States', 'States', 'United'],
['sir']]
'''
#print("grouped_nouns_dict = {0}".format(grouped_nouns_dict))
counter_dict = dict(Counter([val for sublist in grouped_nouns_dict.values() for val in sublist]))
#print("counter={0}".format(counter_dict))
names_all = list(set([val for sublist in grouped_nouns_dict.values() for val in sublist])) # is a list of all unquie names in the list
compare_names_same_format = [val.upper() for val in names_all]
# loop through to group similar elements
gne_list_of_lists = grouped_nouns_dict.values()
gne_list_of_lists = list(set([item for sublist in gne_list_of_lists for item in sublist])) # creates a list of unquie names
import difflib
from difflib import SequenceMatcher
gne_name_group = []
# find most similar ['Professor', 'Professor Aronnax'], ['Aronnax', 'Mr Aronnax', 'Pierre Aronnax']
for gne in gne_list_of_lists:
for g in gne.split():
compared = difflib.get_close_matches(g, gne_list_of_lists)
if compared != []:
gne_name_group.append(compared)
subgrouping = []
#print("\ngne_list_of_lists: {0}".format(gne_list_of_lists))
if len(gne_list_of_lists) > 1: # if there is only one name in all the text (debugging short texts)
for gne in gne_list_of_lists:
sublist = []
if len(gne.split()) == 1 and len(gne.split()[0]) > 1: # includes only single instance values that are not a single letter
sublist.append(gne.split()) # include values that only appear once in a setence
for i in gne.split():
for gne_2 in gne_list_of_lists:
if i in gne_2 and i != gne_2 and (i != [] or gne_2 != []):
chapter_titles = ["CHAPTER", "Chapter", "Volume", "VOLUME"]
# only save words that don't include the chapter titles
found_in_i = any(val in chapter_titles for val in i.split())
found_in_gne2 = any(val in chapter_titles for val in gne_2.split())
if found_in_i or found_in_gne2:
# 'CHAPTER XXII Mr Rochester' -> 'Mr Rochester'
for title in chapter_titles:
if found_in_i:
#print(" ".join(i.split()[2:]))
i = " ".join(i.split()[2:])
break
if found_in_gne2:
#print(" ".join(gne_2.split()[2:]))
gne_2 = " ".join(gne_2.split()[2:])
break
if i != '' and gne_2 != '':
#print("FINAL APPEND={0}\n".format((i, gne_2)))
sublist.append([i, gne_2])
else:
if gne_2 != i:
if gne_2 not in i:
if [gne_2] not in sublist: # only keep one iteration of the name
if len(gne_2) > 1: # exclude single letter
sublist.append([gne_2])
subgrouping.append(sublist)
else:
subgrouping.append(gne_list_of_lists)
final_grouping = []
if len(subgrouping) > 1:
subgrouping = [x for x in subgrouping if x != []]
for subgroup in subgrouping:
final_grouping.append(list(set([item for sublist in subgroup for item in sublist])))
else:
final_grouping = subgrouping # keep the single element
iterate_list_num = list(range(len(final_grouping)))
for i in range(len(final_grouping)):
for num in iterate_list_num:
if num != i:
extend_val = list(set(final_grouping[i]).intersection(final_grouping[num]))
if extend_val:
final_grouping[i].extend(final_grouping[num])
final_grouping[i] = sorted(list(set(final_grouping[i]))) # extend list to include similar elements
#print("\nfinal_grouping: {0}".format(final_grouping))
final_grouping = sorted(final_grouping) # organize and sort
final_grouping = list(final_grouping for final_grouping,_ in itertools.groupby(final_grouping))
#final_grouping = [x for x in final_grouping if x != []] # remove empty lists
#print([item for item in final_grouping if item not in words_to_ignore])
count = 0
character_group_list = []
# remove any word that is part of the 'words_to_ignore' list or is a title by itself
for item in final_grouping:
sublist = []
for i in item:
#print("'{0}' to ignore = {1}".format(i, i in words_to_ignore or i.title() in all_honorific_titles))
if i in words_to_ignore or i.title() in all_honorific_titles:
count += 1
#print("in word to ignore = {0}".format(i))
else:
#print(i)
sublist.append(i)
#print("item = {0}".format(item))
if item[0] in words_to_ignore:
count += 1
if item[0] in words_to_ignore:
pass#print("in word to ignore = {0}".format(item[0]))
else:
#print(item[0])
sublist.append(item[0])
if sublist != []:
character_group_list.append(sublist)
#print("character_group_list: {0}".format(character_group_list))
character_group = [] # only save unquie lists
for i in character_group_list:
if i not in character_group:
character_group.append(i)
#print("\nfinal group: \n{0}".format(final_grouping))
#print("\ncharacter group: \n{0}".format(character_group))
#print(len([item for item in final_grouping if item not in words_to_ignore]))
#print(len(final_grouping))
#print(len(character_group))
#print("count = {0}".format(count))
return character_group
def lookupSubDictionary(shared_ent):
# return a dictionary of proper nouns and surrounding values for one-shot look up
'''
{"Scarlett O'Hara": ["O'Hara", 'Scarlett'], 'Tarleton': ['Tarleton'],
"O'Hara": ["Scarlett O'Hara", 'Scarlett'], 'Scarlett': ["Scarlett O'Hara", "O'Hara"],
'Coast': ['Coast']}
'''
sub_dictionary_lookup = defaultdict(list)
for group in shared_ent:
iterate_list_num = list(range(len(group)))
for i in range(len(group)):
for j in iterate_list_num:
if i != j:
sub_dictionary_lookup[group[i]].append(group[j])
if len(group) == 1:
sub_dictionary_lookup[group[i]].append(group[i]) # for single instances, store {'Tarleton':'Tarleton'{ as its own reference
return dict(sub_dictionary_lookup)
def mostCommonGNE(gne_grouped_dict):
# find the longest most common version of a name in gnes to become the global
#print("\nGlobal GNE")
#for key, value in gne_grouped_dict.iteritems():
# print(key, value)
#print(gne_grouped_dict.values())
pass
########################################################################
## INDEX PRONOUNS
def findPronouns(pos_dict):
# return the sentence index and pronouns for each sentence
#{0: ['he', 'himself', 'his'], 1: ['He', 'his', 'he', 'his', 'he', 'his']}
pos_type_lst = []
for row, pos_named in pos_dict.iteritems():
if "PRP" in pos_named.XPOSTAG:
pos_type_lst.append((int(pos_named.SENTENCE_INDEX), int(pos_named.ID), pos_named.FORM, int(pos_named.SENTENCE_LENGTH), pos_named.XPOSTAG))
total_sentence_indices = list(set([i[0] for i in pos_type_lst]))
sub_sentences = []
for index in total_sentence_indices:
# create sub sentences for each sentence [[0], [1])
sub_sentences.append([x for x in pos_type_lst if x[0] == index])
grouped_pronouns = {}
for pronoun_group in sub_sentences:
pronoun_lst = []
for pronoun in pronoun_group:
pronoun_lst.append(pronoun[2])
grouped_pronouns[pronoun[0]] = pronoun_lst
return grouped_pronouns
def coreferenceLabels(filename, csv_file, character_entities_dict, global_ent, pos_dict):
# save into csv for manual labelling
# TODO: set up with average paragraph length as size_sentences
size_sentences = 21000000000 # looking at x sentences at a time (could be automatically re-adjusted to fix max size of text)
# set to large number so it runs through all sentences (could be set to look at x number of sentences)
rows_of_csv_tuple = csv_file.values()
all_sentences_in_csv = list(set([int(word.SENTENCE_INDEX) for word in csv_file.values()]))
if size_sentences > max(all_sentences_in_csv)+1: # do not go out of range while creating sentences
size_sentences = max(all_sentences_in_csv)+1
print("Size of sentence for manual tagging = {0}".format(size_sentences))
# save chucks of text (size sentences = how many sentences in each chunk of text)
sub_sentences_to_tag = [all_sentences_in_csv[i:i + size_sentences] for i in xrange(0, len(all_sentences_in_csv), size_sentences)]
#print("character entities keys: {0}\n".format(character_entities_dict.keys()))
#print("\n")
row_dict = {} # to print data into csv
gne_index = 0 # display word of interst as [Name]_index
pronoun_index = 0 # display word of interst as [Pronoun]_index
for sentences_tag in sub_sentences_to_tag:
#print(sentences_tag)
# Test that csv is in order
#from itertools import groupby
#from operator import itemgetter
#for k, g in groupby(enumerate(sentences_tag), lambda (i, x): i-x):
# #print(len(map(itemgetter(1), g)))
# print(map(itemgetter(1), g))
# print("\n")
# #print(len(sentences_tag), size_sentences)
if len(sentences_tag) == size_sentences: # ignores sentences at the end that aren't the right length
sentences_in_order = ''
for i in range(sentences_tag[0], sentences_tag[-1]+1):
new_sentence_to_add = list(set([row.SENTENCE for row in rows_of_csv_tuple if row.SENTENCE_INDEX == str(i)]))[0]
if i+1 < sentences_tag[-1]+1:
next_sentence_check = list(set([row.SENTENCE for row in rows_of_csv_tuple if row.SENTENCE_INDEX == str(i+1)]))[0]
if len(next_sentence_check) == 1:
#print("old: {0}".format(new_sentence_to_add))
#print("NEXT IS NEARLY EMPTY, APPEND TO PREVIOUS SENTENCE: '{0}'".format(next_sentence_check))
new_sentence_to_add += next_sentence_check # add the final dialouge tag into the previous sentence
#print("new: {0}".format(new_sentence_to_add))
# returns a sentence in range
new_sentence_to_add = " {0} ".format(new_sentence_to_add) # add whitespace to the begining to find pronouns that start a sentence
if "," in new_sentence_to_add:
new_sentence_to_add = new_sentence_to_add.replace(",", " , ")
if "\"" in new_sentence_to_add:
new_sentence_to_add = new_sentence_to_add.replace("\"", " \" ")
# add space to identify pronouns/nouns at the end of a sentence
if "!" in new_sentence_to_add:
new_sentence_to_add = new_sentence_to_add.replace("!", " ! ")
if "?" in new_sentence_to_add:
new_sentence_to_add = new_sentence_to_add.replace("?", " ? ")
# tag pronouns first (from pos_dict)
if i in pos_dict.keys():
for pronoun in pos_dict[i]: # for all pronouns within the given sentence
total_found = re.findall(r'\b{0}\b'.format(pronoun), new_sentence_to_add)
if re.search(r' \b{0}\b '.format(pronoun), new_sentence_to_add): # match full word
for tf in range(len(total_found)):
new_sentence_to_add = new_sentence_to_add.replace(" {0} ".format(pronoun), " <{0}>_p{1} ".format(pronoun, pronoun_index), tf+1)
pronoun_index += 1
# tag proper nouns
found_longest_match = ''
gne_found_in_sentence = False # if found, print and update the sentence value
lst_gne = []
lst_gne = [gne_name for gne_name in character_entities_dict.keys() if gne_name in new_sentence_to_add]
lst_gne = [x for x in lst_gne if x != []]
index_range_list = [] # compare each index values
all_index_values = [] # contains all index values of gnes
to_remove = [] # if a value is encompassed, it should be removed
if len(lst_gne) > 0:
#print("\nlst_gne = {0}".format(lst_gne))
for gne in lst_gne: # create the index values for each enitity
#print("'{0}' in {1}".format(gne, new_sentence_to_add))
search_item = re.search(r"\b{0}\b".format(gne), new_sentence_to_add)
if not search_item: # if it return none
break # skip item if not found
else:
start = search_item.start() # store the start index of the gne
end = search_item.end() # store the end index of the gne
index_range_word = [start, end]
all_index_values.append(index_range_word)
if len(index_range_list) == 0: # useful for debugging
#print("FIRST GNE {0} has index {1}".format(gne, index_range_word))
pass
else:
for range_index in index_range_list: # the index of the value is stored and new words are check to see if they are contained wtihing
# example: united is within 'united states'
if len(lst_gne) > 1:
if (range_index[0] == index_range_word[0]) and (index_range_word[1] == range_index[1]):
pass
else:
if (range_index[0] <= index_range_word[0]) and (index_range_word[1] <= range_index[1]):
#print("{0} <= {1}-{2} <= {3}".format(range_index[0], index_range_word[0], index_range_word[1], range_index[1]))
#print("{0} IS CONTAINED BY GNE = {1}\n".format(gne, new_sentence_to_add[range_index[0]:range_index[1]]))
to_remove.append(index_range_word)
if (index_range_word[0] <= range_index[0]) and(index_range_word[1] >= range_index[1]):
#pass # reprsents the larger word that encompassing the smaller word
#print("{0} <= {1}-{2} <= {3}".format(index_range_word[0], range_index[0],range_index[1], index_range_word[1]))
#print("{0} IS EMCOMPASSED BY GNE = {1}\n".format(gne, new_sentence_to_add[index_range_word[0]:index_range_word[1]]))
to_remove.append(range_index)
#print("{0} <= {1} = {2}".format(index_range_word[0], range_index[0], index_range_word[0] <= range_index[0]))
#print("{0} >= {1} = {2}".format(index_range_word[1], range_index[1], index_range_word[1] >= range_index[1]))
index_range_list.append(index_range_word)
#print("remove index values = {0}".format(to_remove))
#print("largest gne index values ALL = {0}".format(all_index_values))
all_index_values = sorted([x for x in all_index_values if x not in to_remove]) # index in order based on start value
#print("shared (with removed encompassed) = {0}".format(all_index_values)) # remove all encompassed elements
updated_index = []
new_characters_from_update = 0 # new characters to keep track of character length when indexing
repeats_to_find = []
for counter, index_val in enumerate(all_index_values):
if counter > 0:
new_characters_from_update = len("<>_n ")*counter
start_word = index_val[0] + new_characters_from_update
end_word = index_val[1] + new_characters_from_update
updated_index.append([start_word, end_word])
find_repeats = new_sentence_to_add[start_word:end_word]
repeats_to_find.append(find_repeats)
replacement_string = "<{0}>_n ".format(new_sentence_to_add[start_word:end_word])
new_sentence_to_add = "".join((new_sentence_to_add[:start_word], replacement_string, new_sentence_to_add[end_word:]))
sub_counter = counter
# add repeated gne values
# if the same name appears more than once in a sentence
sub_counter = 0
new_characters_from_update = 0 # new characters to keep track of character length when indexing
for find_additional in repeats_to_find:
repeat_item = re.finditer(r"\b{0}\b".format(find_additional), new_sentence_to_add)
for m in repeat_item:
index_to_check = [m.start(), m.end()]
if [m.start()-1,m.end()-1] not in updated_index: # check that name hasn't been already assigned
if new_sentence_to_add[m.start()-1] != '<' and new_sentence_to_add[m.end():m.end()+3] != '>_n':
start_word = m.start() + new_characters_from_update
end_word = m.end() + new_characters_from_update
replacement_string = "<{0}>_n ".format(new_sentence_to_add[start_word:end_word])
new_sentence_to_add = "".join((new_sentence_to_add[:start_word], replacement_string, new_sentence_to_add[end_word:]))
sub_counter += 1
new_sent = new_sentence_to_add.split()
# label all proper nouns with an associated index value for noun
for index, word_string in enumerate(new_sent):
if '>_n' in word_string:
if word_string != ">_n":
new_sent[index] = '{0}{1}'.format(word_string, gne_index)
new_sentence_to_add = " ".join(new_sent)
gne_index += 1
new_sentence_to_add = new_sentence_to_add.strip() # remove precending whitespace
new_sentence_to_add = new_sentence_to_add.replace('" ', '"') # edit the speech puncutations
new_sentence_to_add = new_sentence_to_add.replace(' "', '"') # edit the speech puncutations
new_sentence_to_add = new_sentence_to_add.replace('\' ', '\'') # edit the speech puncutations
new_sentence_to_add = new_sentence_to_add.replace(' , ', ', ') # edit the speech puncutations
new_sentence_to_add = new_sentence_to_add.replace(' ! ', '! ') # edit the speech puncutations
new_sentence_to_add = new_sentence_to_add.replace(' ? ', '? ') # edit the speech puncutations
if new_sentence_to_add != '"': # if the value is just the end of a dialouge tag (already included, ignore)
sentences_in_order += new_sentence_to_add + '. '
#print(new_sentence_to_add + '. ')
#print("\nFinal Sentence Format:\n\n{0}".format(sentences_in_order))
saveTagforManualAccuracy(sentences_in_order)
else:
# determine that no sentences are skipped because of csv error
print("Manual Accuracy Tag not created, len(sentences_tag) must equal size_sentences")
exit()
def saveTagforManualAccuracy(sentences_in_order):
## corefernece will call the csv creator for each 'paragraph' of text
given_file = os.path.basename(os.path.splitext(filename)[0]) # return only the filename and not the extension
output_filename = "manualTagging_{0}.csv".format(given_file.upper())
fieldnames = ['FILENAME', 'TEXT',
'FOUND_PROPER_NOUN', 'MISSED_PROPER_NOUN',
'FOUND_PRONOUN', 'MISSED_PRONOUN']
split_sentences_in_list = [e+'.' for e in sentences_in_order.split('.') if e] # split sentence based on periods
split_sentences_in_list.remove(' .') # remove empty sentences
sentence_size = 1 # size of the sentence/paragraph saved in manual tagging (one line for one sentence)
sentence_range = [split_sentences_in_list[i:i+sentence_size] for i in xrange(0, len(split_sentences_in_list), sentence_size)]
# range stores the sentences in list of list based on the size of tag
#print("\n")
#for sentence_tag in sentence_range:
# print(''.join(sentence_tag))
# print(''.join(sentence_tag).count("]_n"))
# print(''.join(sentence_tag).count("]_p"))
# print("\n")
print("opening manual tag: {0}".format('manual_tagging/{0}'.format(output_filename)))
with open('manual_tagging/{0}'.format(output_filename), 'w') as tag_data:
writer = csv.DictWriter(tag_data, fieldnames=fieldnames)
writer.writeheader()
# leave MISSED empty for manual tagging
for sentence_tag in sentence_range:
writer.writerow({'FILENAME': os.path.basename(os.path.splitext(filename)[0]),
'TEXT': ''.join(sentence_tag),
'FOUND_PROPER_NOUN': ''.join(sentence_tag).count(">_n"),
'MISSED_PROPER_NOUN': None,
'FOUND_PRONOUN': ''.join(sentence_tag).count(">_p"),
'MISSED_PRONOUN': None
})
print("{0} create MANUAL TAGGING for CSV".format(output_filename))
def breakTextPandN(manual_tag_dir, loaded_gender_model):
# resolve gender and pronoun noun interactions (resolution)
pronoun_noun_dict = {}
given_file = os.path.basename(os.path.splitext(filename)[0]) # return only the filename and not the extension
# set up dict for pronouns and gender: {'His': 'Male', etc...}
pronoun_gender = {f: 'Female' for f in female_pronouns}
m_pronoun_gender = {m: 'Male' for m in male_pronouns}
pronoun_gender.update(m_pronoun_gender)
n_pronoun_gender = {n: 'Neutral' for n in neutral_pronouns}
pronoun_gender.update(n_pronoun_gender)
#check that all pronouns have been included in the dictionary
test_full_list = female_pronouns + male_pronouns + neutral_pronouns
for i in test_full_list:
if i not in pronoun_gender:
print("'{0}' NOT FOUND IN GENDER DICT".format(i))
tagged_text = [] # store old rows
with open(manual_tag_dir, 'r') as tag_data:
reader = csv.reader(tag_data)
next(reader) # skip header
for row in reader:
tagged_text.append(row[1]) # store the sentence in order
sub_dict_titles = ['full_text', 'found_all_brackets', 'found_proper_name_value',
'found_proper_name_index', 'found_pronoun_value', 'found_pronoun_index']
line_by_line_dict = {}
pronoun_noun_dict = {f: [] for f in sub_dict_titles}
total_sentences_to_check_behind = 3 # TODO: update with pronouns average information
#print("\n")
for line_num, full_text in enumerate(tagged_text):
#print(full_text)
pronoun_noun_dict['full_text'].append([full_text])
find_gne_in_sentence_pattern = r'(?<=\<)(.*?)(?=\>)'
found_all_brackets = re.findall(find_gne_in_sentence_pattern, full_text) # everything together in the order that they appear
pronoun_noun_dict['found_all_brackets'].append([found_all_brackets])
#print('\n')
#print(found_all_brackets)
all_found_name_index = [[m.start(), m.end()] for m in re.finditer(find_gne_in_sentence_pattern, full_text)] # get index of all matches
found_proper_name_value = [full_text[i[0]:i[1]] for i in all_found_name_index if full_text[i[1]+2] is 'n'] # store named ents
found_proper_name_index = [i for i in all_found_name_index if full_text[i[1]+2] is 'n'] # store named index of names
#for index_g in all_found_name_index:
# print(full_text[index_g[0]:index_g[1]])
pronoun_noun_dict['found_proper_name_value'].extend([found_proper_name_value])
pronoun_noun_dict['found_proper_name_index'].extend([found_proper_name_index])
found_pronoun_value = [full_text[i[0]:i[1]] for i in all_found_name_index if full_text[i[1]+2] is 'p'] # store pronouns seperately
found_pronoun_index = [i for i in all_found_name_index if full_text[i[1]+2] is 'p'] # store named index of pronouns
pronoun_noun_dict['found_pronoun_value'].extend([found_pronoun_value])
pronoun_noun_dict['found_pronoun_index'].extend([found_pronoun_index])
# store in a sub-dictionary for each line
line_by_line_dict[line_num] = {'full_text': full_text.strip(),
'found_all_brackets': found_all_brackets,
'found_proper_name_value': found_proper_name_value,
'found_proper_name_index': found_proper_name_index,
'found_pronoun_value': found_pronoun_value,
'found_pronoun_index': found_pronoun_index }
#print("\nfound pronouns index: {0}".format(all_found_name_index))
#for index_g in all_found_name_index:
# print(full_text[index_g[0]:index_g[1]])
#print('\n')
#print(found_proper_name_value)
#for given_name in found_proper_name_value:
# print("{0} is {1}".format(given_name, gender_gne_tree[given_name]))
#print('\n')
#print(found_pronoun_value)
#for pron in found_pronoun_value:
#print("{0} is {1}".format(pron, pronoun_gender[pron.capitalize()]))
# compress dictionray from a list of list to a single list
for key, value in pronoun_noun_dict.iteritems():
pronoun_noun_dict[key] = [item for sublist in value for item in sublist]
if key == 'full_text':
pronoun_noun_dict[key] = [''.join(pronoun_noun_dict[key])] # join the sentences into a single sentence
return pronoun_noun_dict, line_by_line_dict
def determineGenderOfListOfNames(loaded_gender_model, list_of_names):
# use trained model to determine liekly gender of a name and the sub-names to find max likelyhood
#print("DETERMINE GENDER OF: {0}".format(list_of_names))
most_likely_gender_label = [] # label and prob: ['Male', (Female: 0.28571, Male: 0.71429)]
determine_words_to_weight_less = []
female_prob = 0.0
male_prob = 0.0
for full_name in list_of_names:
if bool(re.search(r'\d', full_name)): # if incorrectly labeled a number
# number, give a default tag Male since numbres can't be checked for value
gender_is = 'Male'
else:
found_with_title = False
#print(full_name)
full_name_in_parts = full_name.split(" ")
#print(full_name_in_parts)
if bool(set(full_name_in_parts) & set(connecting_words)): #if connecting words in name
found_connecting_ignore_following = False
for part in full_name_in_parts:
if part in connecting_words:
found_connecting_ignore_following = True
determine_words_to_weight_less.append(part)
if found_connecting_ignore_following and part not in connecting_words: # if word that following connecting word
determine_words_to_weight_less.append(part)
# weight of and the less, but also 'Queen Elizabeth of England', weigh 'of England' less
#print("words to weigh less = {0}".format(determine_words_to_weight_less))
# if name is part of a gendered honorific, return: Mr Anything is a male
if full_name_in_parts[0].title() in male_honorific_titles:
#print("'{0}' contains '{1}' found: Male".format(full_name, full_name_in_parts[0]))
gender_is = 'Male'
male_prob += 1.0
found_with_title = True
if full_name_in_parts[0].title() in female_honorific_titles:
#print("'{0}' contains '{1}' found: Female".format(full_name, full_name_in_parts[0]))
gender_is = 'Female'
female_prob += 1.0
found_with_title = True
# find the name for each part of the name, choose highest
#print("'{0}' not found, calculating a probability...".format(full_name)) # not found in gendered honorifics
# run test on each part of the name, return the largest so that last names don't overly effect
dt = np.vectorize(DT_features) #vectorize dt_features function
weight_last_name_less = 0.3
weight_connecting_words_less = 0.2 # weight words that follow 'of' less
if not found_with_title:
for sub_name in full_name_in_parts:
# determine if the name is likely to be the last name, if so, weight less than other parts of the name
if sub_name in connecting_words:
# do not calculate for titles "Queen of England" shouldn't find for England
break
else:
if sub_name not in ignore_neutral_titles:
# female [0], male [1]
load_prob = loaded_gender_model.predict_proba(dt([sub_name.title()]))[0]
if sub_name in determine_words_to_weight_less:
#print("this should be weighted less = '{0}'".format(sub_name))
load_prob = load_prob*weight_connecting_words_less
else: # if not in connecting words, check if last name
is_a_last_name = isLastName(gne_tree, sub_name)
#print("\tprobability: {0}\n".format(load_prob))
if is_a_last_name: # if last name, weigh less than other names
#print("'{0}' is a last name, will weight less".format(sub_name))
load_prob = load_prob*weight_last_name_less
female_prob += load_prob[0]
male_prob += load_prob[1]
#print("\t updated: f={0}, m={1}".format(female_prob, male_prob))
#if (abs(male_prob - female_prob) < 0.02): #within 2 percent, undeterminex
# gender_is = "UNDETERMINED"
#else:
gender_is = 'Male' if male_prob > female_prob else 'Female'
#print("The subname '{0}' is most likely {1}\nFemale: {2:.5f}, Male: {3:.5f}".format(full_name, gender_is, female_prob, male_prob))
#print("\tFINAL: The list '{0}' is most likely {1}\n\tFemale: {2:.5f}, Male: {3:.5f}\n".format(list_of_names, gender_is, female_prob, male_prob))
return gender_is
def determineGenderNameDict(loaded_gender_model, gne_tree):
# use trained model to determine the likely gender of a name
gender_gne = {}
all_gne_values = gne_tree.keys()
for key, values in gne_tree.iteritems():
for k, v in values.iteritems():
# add all sub_trees to list
all_gne_values += [k]
all_gne_values += v
all_gne_values = [[x] for x in all_gne_values] # covert each element to a list to use generic gender determine function
for full_name in all_gne_values:
gender_is = determineGenderOfListOfNames(loaded_gender_model, full_name)
gender_gne[full_name[0]] = gender_is
return gender_gne
def isLastName(gne_tree, sub_name):
# determine if the name is likely to be the last name
#{'Samsa': ['Samsa'], 'Gregor': ['Gregor', 'Gregor Samsa']}
# last name is the most common last element in a name and has no futher sub-roots
# last name will be weighted less, if there are other elements present
is_last_name = False
for key, value in gne_tree.iteritems():
if sub_name in key:
if sub_name in key.split()[-1] and len(value) > 1: # if in the last position and isn't the only value {'John': ['John']}
is_last_name = True
#print("'{0}' is a last name = {1}".format(sub_name, is_last_name))
return is_last_name
def loadDTModel():
# load saved gender model from gender_name_tagger
from sklearn.externals import joblib # save model to load
model_file_dir = 'gender_name_tagger'
updated_saved_model = [f for f in os.listdir(model_file_dir) if 'pipeline_gender_saved_model' in f][0]
print("LOADING SAVED GENDER NAME MODEL: {0}".format(updated_saved_model))
pipeline_loaded = joblib.load('{0}/{1}'.format(model_file_dir, updated_saved_model))
return pipeline_loaded
def DT_features(given_name):
test_given_name = ['corette', 'corey', 'cori', 'corinne', 'william', 'mason', 'jacob', 'zorro'] #small test
FEATURE_TAGS = ['first_letter',
'first_2_letters',
'first_half',
'last_half',
'last_2_letters',
'last_letter',
'length_of_name']
features_list = []
name_features = [given_name[0], given_name[:2], given_name[:len(given_name)/2], given_name[len(given_name)/2:], given_name[-2:], given_name[-1:], len(given_name)]
#[['z', 'zo', 'zo', 'rro', 'ro', 'o', 5], ['z', 'zo', 'zo', 'rro', 'ro', 'o', 5]]
features_list = dict(zip(FEATURE_TAGS, name_features))
return features_list
def gneHierarchy(character_entities_group, over_correct_for_multiple_title):
# merge gne into a dict for look up
'''
key: Dr Urbino
{'Dr': [['Dr', 'Dr Juvenal Urbino', 'Dr Urbino'], ['Urbino']],
'Urbino': [['Dr', 'Dr Juvenal Urbino', 'Dr Urbino'], ['Urbino']]}
'''
# if there are a name with a different version of the title, include both
if over_correct_for_multiple_title:
character_entities_group, found_similar = addNameWithSameTitle(character_entities_group) # add names with different titles
character_split_group = [x.split() for x in character_entities_group]
character_split_group = sorted(character_split_group, key=len, reverse=True)
gne_tree = defaultdict(dict)
gne_dict_sub = {}
for longer_name in character_split_group:
#print("{0} IS NOT in gne_tree: {1}".format(" ".join(longer_name), gne_tree))
#print("longer: {0}".format(longer_name))
already_in_tree = any(" ".join(longer_name) in g for g in gne_tree)
if not already_in_tree: # if not already in a sub tree
if len(longer_name) > 1 or len(longer_name[0]) > 1: # ignore intials 'C'
#print("base: {0}".format(longer_name))
#print("base: {0}".format(" ".join(longer_name)))
for sub_long_name in longer_name:
gne_tree_word_tree = []
#print("sub: {0}".format(sub_long_name))
gne_tree_word_tree.append(sub_long_name)
for smaller_name in character_entities_group:
name_with_caps = sub_long_name
is_sub_capitalized = sub_long_name.isupper()
if is_sub_capitalized:
name_with_caps = sub_long_name.title()
if name_with_caps in smaller_name.split() and name_with_caps not in connecting_words:
# store only honorific titles that include elements of the same name
# 'Dr Juvenal Urbino' NOT 'Dr Lacides Olivella', but 'Dr Juvenal Urbino' and 'Dr Urbino'
if name_with_caps in all_honorific_titles and len(longer_name) > 1:
if any(i.title() in longer_name for i in smaller_name.split() if i.title() not in all_honorific_titles):
#print("\t\tindex: {0}".format(smaller_name.split().index(sub_long_name.title())))
sub_name_join = " ".join(smaller_name.split()[smaller_name.split().index(sub_long_name.title()):])
#print("\t\t\n\n\nnewfound: {0}".format(sub_name_join))
#print("sub_name_join.title() not in gne_tree_word_tree = {0}".format(sub_name_join.title() not in gne_tree_word_tree))
if sub_name_join.title() not in gne_tree_word_tree:
gne_tree_word_tree.append(sub_name_join)
if over_correct_for_multiple_title:
# add other versions of the same name: "Capt Nemo" and "Captian Nemo"
for potential_conflict in found_similar:
#print("sub_name_join in potential_conflict = {0}".format(sub_name_join in potential_conflict))
if sub_name_join in potential_conflict:
for other_name in potential_conflict:
if other_name != sub_name_join:
if len(other_name.split()) > 1:
sub_name_first_name = sub_name_join.split()[1]
sub_name_last_name = sub_name_join.split()[-1]
other_name_first_name = other_name.split()[1]
other_name_last_name = other_name.split()[-1]
if sub_name_first_name == other_name_first_name or other_name_last_name == sub_name_last_name:
#print("current = {0}".format(sub_name_join))