def variativity_replacement(sentence, verb_token, object_token, object_index, verb_index, parsed_tokens, related_verb, lvc_phrase, catvar_file, adjective_adverb_dict, no_adverb_set, object_preposition_dict, no_preposition_objects, subject_person, subject_number):
verb_tag = verb_token.tag_
final_phrase = u''
set_negation = False
set_perfect = False
set_progressive = False
set_present_tense = False
set_past_tense = False
set_future_tense = False
set_third_person = False
set_modal = False
set_gerund_only = False
modal_verb = ''
set_having_participle = False
nodebox_negate = False
nodebox_participle = False
nodebox_gerund = False
nodebox_simple_present_third_person = False
nodebox_simple_present_other_person = False
nodebox_simple_past = False
list_of_auxiliary_verbs = []
for token in parsed_tokens:
if token.head is verb_token and token.dep_ in ['aux', 'neg']:
list_of_auxiliary_verbs.append(token.orth_.lower())
if 'has' in list_of_auxiliary_verbs:
set_present_tense = True
set_third_person = True
elif 'have' in list_of_auxiliary_verbs:
set_present_tense = True
elif 'had' in list_of_auxiliary_verbs:
set_past_tense = True
if 'been' in list_of_auxiliary_verbs:
set_perfect = True
set_progressive = True
if set(['will', '\'ll', 'shall']) & set(list_of_auxiliary_verbs):
set_future_tense = True
if 'did' in list_of_auxiliary_verbs:
set_past_tense = True
if 'does' in list_of_auxiliary_verbs:
set_present_tense = True
set_third_person = True
if 'do' in list_of_auxiliary_verbs:
set_present_tense = True
if 'can' in list_of_auxiliary_verbs:
set_modal = True
modal_verb = 'can'
set_present_tense = True
if 'could' in list_of_auxiliary_verbs:
set_modal = True
modal_verb = 'could'
if 'should' in list_of_auxiliary_verbs:
set_modal = True
modal_verb = 'should'
if 'was' in list_of_auxiliary_verbs:
set_past_tense = True
set_third_person = True
if 'were' in list_of_auxiliary_verbs:
set_past_tense = True
#third person singular form, present tense
if verb_tag == u'VBZ':
set_third_person = True
set_present_tense = True
nodebox_simple_present_third_person = True
#present tense, plural subject
elif verb_tag == u'VBP':
set_present_tense = True
nodebox_simple_present_other_person = True
#gerund
elif verb_tag == u'VBG':
nodebox_gerund = True
set_progressive = True
#gerund without auxiliary
if not list_of_auxiliary_verbs:
set_gerund_only = True
#participle. 'having' + participle is handled separately from the regular perfect tenses
elif verb_tag == u'VBN' and (set(['has', '\'s', 'have', '\'d', 'had', '\'d', 'having']) & set(list_of_auxiliary_verbs)):
nodebox_participle = True
if 'having' in list_of_auxiliary_verbs:
set_having_participle = True
else:
set_perfect = True
#a simple past tense (VBD) is commonly mistagged as a perfect (VBN)
elif verb_tag == u'VBD' or verb_tag == u'VBN':
set_past_tense = True
nodebox_simple_past = True
# the exact phrase to replace in the sentence
phrase_to_replace = lvc_phrase
aux_verb_buffer = []
for token in parsed_tokens:
if token.head is verb_token and token.dep_ in ['aux', 'neg']:
aux_verb_buffer.append(token.orth_)
if aux_verb_buffer:
for aux_verb in reversed(aux_verb_buffer):
phrase_to_replace = aux_verb + ' ' + phrase_to_replace
#currently omitting cases like 'had HE been going to the station', where all the auxiliary verbs and the main verb are not contiguous
if phrase_to_replace not in sentence:
return ''
list_of_modifiers = [token for token in parsed_tokens if token.head is object_token and token.dep_ != "det" and get_index_in_list(parsed_tokens, token) in range(verb_index, object_index)]
list_of_modifiers.extend([token for token in parsed_tokens if token.head is verb_token and token is not object_token and token.dep_ != "det" and get_index_in_list(parsed_tokens, token) in range(verb_index, object_index)])
#handle separately numerical and adjectival modifiers, and also look for clues where the verb must occur in its negated form
#adjectival modifiers
list_of_amod_modifiers = [token for token in list_of_modifiers if token.dep_ == "amod" and token.head is object_token]
#numerical modifiers
list_of_nummod_modifiers = [token for token in list_of_modifiers if token.dep_ == "nummod" and token.head is object_token and token.orth_.lower() not in ['0', 'zero']]
#clues for negation- a numerical modifier that says 'zero', or a determiner for the object which says 'no'. More can, of course, be added
list_of_negative_modifiers = [token for token in parsed_tokens if token.head is object_token and ((token.dep_ == 'nummod' and token.orth_.lower() in ['0', 'zero']) or (token.dep_ == 'det' and token.orth_.lower() == 'no'))]
adjectival_modification = ''
numerical_modification = ''
dative_object_string = ''
#handle negation generation
if list_of_negative_modifiers:
#print list_of_negative_modifiers[0].orth_, list_of_negative_modifiers[0].dep_, list_of_negative_modifiers[0].head.orth_, list_of_negative_modifiers[0].tag_
set_negation = True
nodebox_negate = True
"""negation_list = [token for token in parsed_tokens if token.dep_ == 'neg' and token.head is verb_token and get_index_in_list(parsed_tokens, token) < object_index]
if negation_list:
print 'Negation case 2'
print negation_list[0].orth_, negation_list[0].dep_, negation_list[0].head.orth_, negation_list[0].tag_
set_negation = True
nodebox_negate = True"""
#convert adjectives to adverbs
if list_of_amod_modifiers:
main_amod_modifier_token = list_of_amod_modifiers[0]
amod_modification = adjectival_modifier(parsed_tokens, main_amod_modifier_token, catvar_file, adjective_adverb_dict, no_adverb_set)
if amod_modification:
adjectival_modification = amod_modification
#convert numerical modifiers by post-appending 'times'
if list_of_nummod_modifiers:
main_nummod_modifier_token = list_of_nummod_modifiers[0]
nummod_modification = numerical_modifier(parsed_tokens, main_nummod_modifier_token)
if nummod_modification:
numerical_modification = nummod_modification
#call to simplenlg program to generate verb form
"""simplenlg_command = 'java -jar SimpleNLGPhraseGenerator.jar ' + str(set_negation) + ' ' + str(set_present_tense) + ' ' + str(set_past_tense) + ' ' + str(set_future_tense) + ' ' + str(set_third_person) + ' ' + str(set_perfect) + ' ' + str(set_progressive) + ' ' + str(set_modal) + ' ' + str(set_having_participle) + ' ' + str(subject_person) + ' ' + str(subject_number) + ' ' + related_verb + ' ' + modal_verb
status = os.system(simplenlg_command + ' > verb_phrase_output')
print status
print simplenlg_command
print sentence
temp_file = open('verb_phrase_output', 'r')
#content = check_output(simplenlg_command)
content = temp_file.readline()
phrases = content.split(' ||| ')
temp_file.close()
os.system('rm verb_phrase_output')
#if there was an error in the simplenlg programme
if len(phrases) != 2:
return ''"""
final_phrase_active = nodebox_verb_conjugator(related_verb, aux_verb_buffer, subject_person, subject_number, nodebox_negate, nodebox_participle, nodebox_gerund, nodebox_simple_present_third_person, nodebox_simple_present_other_person, nodebox_simple_past) #phrases[0]
final_phrase_passive = nodebox_verb_conjugator_passive(related_verb, final_phrase_active, aux_verb_buffer, subject_person, subject_number, nodebox_negate, nodebox_participle, nodebox_gerund, nodebox_simple_present_third_person, nodebox_simple_present_other_person, nodebox_simple_past) #phrases[1]
if final_phrase_passive is None:
return ''
#if the verb was only a gerund
"""if (set_gerund_only):
print "Sentence is- " + sentence
print "Active phrase is- " + final_phrase_active
final_phrase_active = final_phrase_active.split(' ')[1]
final_phrase_passive = ' '.join(final_phrase_passive.split(' ')[1:])
#'having' + participle
if (set_having_participle):
final_phrase_active = 'having ' + final_phrase_active
final_phrase_passive = 'having ' + final_phrase_passive"""
#WAIT, THIS IS PROBLEMATIC
list_of_dative_objects_within_phrase = [token for token in parsed_tokens if token.dep_ == 'dative' and token.head is verb_token and get_index_in_list(parsed_tokens, token) in range(verb_index, object_index)]
if len(list_of_dative_objects_within_phrase) == 1:
dative_token = list_of_dative_objects_within_phrase[0]
dative_object_string = dative_token.orth_
dative_object_buffer = []
for token in parsed_tokens:
if token.head is dative_token:
dative_object_buffer.append(token.orth_)
if dative_object_buffer:
for dative_object_modifier in reversed(dative_object_buffer):
dative_object_string = dative_object_modifier + ' ' + dative_object_string
if adjectival_modification and adjectival_modification.strip()[-2:] == 'ly':
final_phrase_active = ' '.join(final_phrase_active.split()[:-1]) + ' ' + adjectival_modification + ' ' + final_phrase_active.split()[-1]
final_phrase_passive = ' '.join(final_phrase_passive.split()[:-1]) + ' ' + adjectival_modification + ' ' + final_phrase_passive.split()[-1]
if dative_object_string:
final_phrase_active += ' ' + dative_object_string
final_phrase_passive += ' by ' + dative_object_string
if adjectival_modification and not (adjectival_modification[-2:] == 'ly'):
final_phrase_active += ' ' + adjectival_modification
final_phrase_passive += ' ' + adjectival_modification
if numerical_modification:
final_phrase_active += ' ' + numerical_modification
final_phrase_passive += ' ' + numerical_modification
final_phrase_active = final_phrase_active.strip()
final_phrase_passive = final_phrase_passive.strip()
#now to wonder whether to omit the preposition or not
setup_verb_prep_combination_dict()
list_of_preposition_tokens = [token for token in parsed_tokens if token.tag_ == u'IN' and (token.dep_ == 'dative' or token.dep_ == 'prep') and token.orth_.lower() in common_prepositions and (token.head is verb_token or token.head is object_token) and get_index_in_list(parsed_tokens, token) in range(verb_index + 1, object_index + 2)]
if list_of_preposition_tokens:
preposition_token = list_of_preposition_tokens[0]
verb_prep_caps = verb_token.lemma_.upper() + '_' + preposition_token.orth_.upper()
if verb_prep_caps in verb_prep_combination_dict and verb_prep_combination_dict[verb_prep_caps] != '-':
if verb_prep_combination_dict[verb_prep_caps] == '+':
#reconstruct sentence without that preposition
sentence = ''
for token in parsed_tokens:
if token is not preposition_token:
sentence += token.orth_ + ' '
sentence = sentence.strip()
#check if preposition to be added
additional_preposition = select_preposition_for_object(related_verb, object_preposition_dict, no_preposition_objects)
if additional_preposition:
print 'Adding preposition ' + additional_preposition
final_phrase_active = final_phrase_active.strip() + ' ' + additional_preposition
final_phrase_passive = final_phrase_passive.strip() + ' ' + additional_preposition
elif verb_prep_combination_dict[verb_prep_caps] == 'A':
probabilities = urllib2.urlopen(urllib2.Request('http://weblm.research.microsoft.com/rest.svc/bing-body/2013-12/5/jp?u=' + microsoft_weblm_api_key + '&format=json', en.verb.past(related_verb) + '\n' + en.verb.past(related_verb) + ' ' + preposition_token.orth_)).read().split(',')
verb_probability = float(probabilities[0][1:])
verb_prep_probability = float(probabilities[1][:-1])
#print en.verb.past(related_verb), verb_probability
#print en.verb.past(related_verb) + ' ' + preposition_token.orth_, verb_prep_probability
#major changes may be needed here
if verb_probability - verb_prep_probability > 1.75:
sentence = ''
for token in parsed_tokens:
if token is not preposition_token:
sentence += token.orth_ + ' '
sentence = sentence.strip()
#check if preposition to be added
additional_preposition = select_preposition_for_object(related_verb, object_preposition_dict, no_preposition_objects)
if additional_preposition:
print 'Adding preposition ' + additional_preposition
final_phrase_active = final_phrase_active.strip() + ' ' + additional_preposition
final_phrase_passive = final_phrase_passive.strip() + ' ' + additional_preposition
elif verb_prep_caps not in verb_prep_combination_dict:
#check if preposition to be added
additional_preposition = select_preposition_for_object(related_verb, object_preposition_dict, no_preposition_objects)
if additional_preposition:
print 'Adding preposition ' + additional_preposition
final_phrase_active = final_phrase_active.strip() + ' ' + additional_preposition
final_phrase_passive = final_phrase_passive.strip() + ' ' + additional_preposition
#if the object is coordinated in a zeugma, replicate the verb to feature with the coordinated object
zeugma_heads = [token for token in parsed_tokens if token.dep_ == 'conj' and token.head is object_token and [other_token for other_token in parsed_tokens if other_token.orth_.lower() in ['and', 'or'] and other_token.dep_ == 'cc' and other_token.head is object_token]]
if zeugma_heads:
zeugma_conjunction = [other_token for other_token in parsed_tokens if other_token.orth_.lower() in ['and', 'or'] and other_token.dep_ == 'cc' and other_token.head is object_token][0]
conjunction_index = get_index_in_list(parsed_tokens, zeugma_conjunction)
sentence = sentence.replace(parsed_tokens[conjunction_index-1].orth_ + ' '+ zeugma_conjunction.orth_ + ' ' + parsed_tokens[conjunction_index+1].orth_, parsed_tokens[conjunction_index-1].orth_ + ' '+ zeugma_conjunction.orth_ + ' ' + verb_token.orth_ + ' ' + parsed_tokens[conjunction_index+1].orth_)
return [sentence.replace(phrase_to_replace, final_phrase_active), sentence.replace(phrase_to_replace, final_phrase_passive)]
if len(sentence) > 250: sentence = u'' candidate_object_token_numbers[:] = [] continue #parsing the sentence parsed_tokens = nlp_pipeline(sentence) #add all probable direct object candidates to a list for index in range(len(parsed_tokens)): #if there's a direct object, to start with if (parsed_tokens[index].dep_.strip() == "dobj"): #if the verb belongs to the list of allowed verbs if parsed_tokens[index].head.lemma_ in allowed_verbs: #if the object's head verb is not more than 6 tokens behind the direct object poss_head_index = get_index_in_list(parsed_tokens, parsed_tokens[index].head) if poss_head_index < index and index - poss_head_index < 7: #if the object is not a punctuation mark or number-like or url-like if not(parsed_tokens[index].check_flag(IS_PUNCT) or parsed_tokens[index].check_flag(LIKE_URL) or parsed_tokens[index].check_flag(LIKE_NUM)): #if the noun has a verb in the same cluster from catvar if noun_to_verb_in_catvar(catvar_file, parsed_tokens[index].lemma_.lower(), catvar_noun_dict, catvar_no_verb_set): #check if the verb has a conjugation in our verb conjugator if [verb for verb in catvar_noun_dict[parsed_tokens[index].lemma_.lower()] if verb in conjugated_verbs]: #check whether the nominal form of this noun appears much more frequently compared to that of the verbal form in the BNC if compare_lemma_verb_noun_frequencies(parsed_tokens[index].lemma_.lower(), catvar_noun_dict, bnc_noun_frequencies, bnc_verb_frequencies): #add this token to the list of possible light verb objects for this sentence: there may be multiple candidate_object_token_numbers.append(index) #now consider each candidate, construct verb phrase, noun phrase, lvc phrase for object_index in candidate_object_token_numbers: #some initializations