def stop_word_tagger_hub(input_dict):
"""
Apply the *stop_word_tagger* object on the Annotated Document Corpus (*adc*):
1. first select only annotations of type Token Annotation *element_annotation*,
2. apply the stop_word tagger
3. create new annotations *output_feature* with the outputs of the stop word tagger.
:param adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
:param stop_word_tagger: A python dictionary containing the stop word tagger object and its arguments.
:param element_annotation: Which annotated part of document to be searched for stopwords.
:param output_features: How to annotate the newly discovered stop word features.
:returns adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
"""
if isinstance(input_dict['stop_word_tagger'],LatinoObject):
from ...latino.library_gen import latino_tag_adcstopwords
input_dict['tagger']=input_dict['stop_word_tagger'] #TODO temporary
return executeFunction.apply_async([latino_tag_adcstopwords,input_dict],queue="windows").wait() if settings.USE_WINDOWS_QUEUE \
else latino_tag_adcstopwords(input_dict)
else:
adc = input_dict['adc']
tagger_dict = input_dict['stop_word_tagger']
input_annotation = input_dict['element_annotation']
output_annotation = input_dict['output_feature']
return universal_word_tagger_hub(adc,tagger_dict,input_annotation,output_annotation)
def stop_word_tagger_hub(input_dict):
"""
Apply the *stop_word_tagger* object on the Annotated Document Corpus (*adc*):
1. first select only annotations of type Token Annotation *element_annotation*,
2. apply the stop_word tagger
3. create new annotations *output_feature* with the outputs of the stop word tagger.
:param adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
:param stop_word_tagger: A python dictionary containing the stop word tagger object and its arguments.
:param element_annotation: Which annotated part of document to be searched for stopwords.
:param output_features: How to annotate the newly discovered stop word features.
:returns adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
"""
if isinstance(input_dict['stop_word_tagger'], LatinoObject):
from ...latino.library_gen import latino_tag_adcstopwords
input_dict['tagger'] = input_dict['stop_word_tagger'] #TODO temporary
return executeFunction.apply_async([latino_tag_adcstopwords,input_dict],queue="windows").wait() if settings.USE_WINDOWS_QUEUE \
else latino_tag_adcstopwords(input_dict)
else:
adc = input_dict['adc']
tagger_dict = input_dict['stop_word_tagger']
input_annotation = input_dict['element_annotation']
output_annotation = input_dict['output_feature']
return universal_word_tagger_hub(adc, tagger_dict, input_annotation,
output_annotation)
def stem_lemma_tagger_hub(input_dict):
if isinstance(input_dict['tagger'],LatinoObject): #check if this is a latino object
from ...latino.library_gen import latino_tag_adc_stem_lemma
return latino_tag_adc_stem_lemma(input_dict) if not settings.USE_WINDOWS_QUEUE \
else executeFunction.apply_async([latino_tag_adc_stem_lemma,input_dict],queue="windows").wait()
else:
adc = input_dict['adc']
tagger_dict = input_dict['tagger']
input_annotation = input_dict['element_annotation']
output_annotation = input_dict['output_feature']
return universal_word_tagger_hub(adc,tagger_dict,input_annotation,output_annotation)
def stem_lemma_tagger_hub(input_dict):
if isinstance(input_dict['tagger'],
LatinoObject): #check if this is a latino object
from ...latino.library_gen import latino_tag_adc_stem_lemma
return latino_tag_adc_stem_lemma(input_dict) if not settings.USE_WINDOWS_QUEUE \
else executeFunction.apply_async([latino_tag_adc_stem_lemma,input_dict],queue="windows").wait()
else:
adc = input_dict['adc']
tagger_dict = input_dict['tagger']
input_annotation = input_dict['element_annotation']
output_annotation = input_dict['output_feature']
return universal_word_tagger_hub(adc, tagger_dict, input_annotation,
output_annotation)
def tokenizer_hub(input_dict):
"""
Apply the *tokenizer* object on the Annotated Document Corpus (*adc*):
1. first select only annotations of type *input_annotation*,
2. apply the tokenizer
3. create new annotations *output_annotation* with the outputs of the tokenizer.
:param adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
:param tokenizer: A python dictionary containing the Tokenizer object and its arguments.
:param input_annotation: Which annotated part of document to be splitted.
:param output_annotation: How to annotate the newly discovered tokens.
:returns adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
"""
tokenizer_dict = input_dict['tokenizer']
if type(tokenizer_dict) != dict:
from workflows.tasks import executeFunction
from tf_latino.latino.library_gen import latino_tokenize_words
return latino_tokenize_words(input_dict) if not settings.USE_WINDOWS_QUEUE \
else executeFunction.apply_async([latino_tokenize_words,input_dict],queue="windows").wait()
else:
tokenizer = tokenizer_dict['object']
args = tokenizer_dict.get('args', [])
kwargs = tokenizer_dict.get('kargs', {})
input_annotation = input_dict['input_annotation']
output_annotation = input_dict['output_annotation']
adc = input_dict['adc']
docs_count = len(adc.documents)
for i, document in enumerate(adc.documents):
if document.features['contentType'] == "Text":
if not document.text:
pass
for annotation, subtext in document.get_annotations_with_text(
input_annotation): #all annotations of this type
new_token_spans = tokenizer.span_tokenize(
subtext, *args, **kwargs)
for starts_at, ends_at in new_token_spans:
document.annotations.append(
Annotation(annotation.span_start + starts_at,
annotation.span_start + ends_at - 1,
output_annotation))
if i % 100 == 0:
print int((i + 1) * 1.0 / docs_count * 100)
#widget.progress = int((i+1)*1.0/*100)
#widget.save()
return {'adc': adc}
def stem_lemma_tagger_hub(input_dict):
if input_dict[
'tagger'].__class__.__name__ == "LatinoObject": #check if this is a latino object
from tf_latino.latino.library_gen import latino_tag_adc_stem_lemma
from workflows.tasks import executeFunction
return latino_tag_adc_stem_lemma(input_dict) if not settings.USE_WINDOWS_QUEUE \
else executeFunction.apply_async([latino_tag_adc_stem_lemma,input_dict],queue="windows").wait()
else:
adc = input_dict['adc']
tagger_dict = input_dict['tagger']
input_annotation = input_dict['element_annotation']
pos_annotation = input_dict.get('pos_annotation')
output_annotation = input_dict['output_feature']
return universal_word_tagger_hub(adc, tagger_dict, input_annotation,
output_annotation, pos_annotation)
def tokenizer_hub(input_dict):
"""
Apply the *tokenizer* object on the Annotated Document Corpus (*adc*):
1. first select only annotations of type *input_annotation*,
2. apply the tokenizer
3. create new annotations *output_annotation* with the outputs of the tokenizer.
:param adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
:param tokenizer: A python dictionary containing the Tokenizer object and its arguments.
:param input_annotation: Which annotated part of document to be splitted.
:param output_annotation: How to annotate the newly discovered tokens.
:returns adc: Annotated Document Corpus (workflows.textflows.DocumentCorpus)
"""
tokenizer_dict = input_dict['tokenizer']
if type(tokenizer_dict)!=dict:
from ...latino.library_gen import latino_tokenize_words
return latino_tokenize_words(input_dict) if not settings.USE_WINDOWS_QUEUE \
else executeFunction.apply_async([latino_tokenize_words,input_dict],queue="windows").wait()
else:
tokenizer=tokenizer_dict['object']
args=tokenizer_dict.get('args',[])
kwargs=tokenizer_dict.get('kargs',{})
input_annotation = input_dict['input_annotation']
output_annotation = input_dict['output_annotation']
adc = input_dict['adc']
docs_count=len(adc.documents)
for i,document in enumerate(adc.documents):
if document.features['contentType'] == "Text":
if not document.text:
pass
for annotation,subtext in document.get_annotations_with_text(input_annotation): #all annotations of this type
new_token_spans=tokenizer.span_tokenize(subtext,*args,**kwargs)
for starts_at,ends_at in new_token_spans:
document.annotations.append(Annotation(annotation.span_start+starts_at,annotation.span_start+ends_at-1,output_annotation))
if i%100==0:
print int((i+1)*1.0/docs_count*100)
#widget.progress = int((i+1)*1.0/*100)
#widget.save()
return {'adc': adc}
def pos_tagger_hub(input_dict):
if isinstance(input_dict['pos_tagger'],LatinoObject): #check if this is a latino object
from ...latino.library_gen import latino_pos_tag
adc= executeFunction.apply_async([latino_pos_tag,input_dict],queue="windows").wait()['adc'] \
if settings.USE_WINDOWS_QUEUE else latino_pos_tag(input_dict)
else:
adc= universal_sentence_tagger_hub(input_dict)['adc']
number_of_letters=int(input_dict['num_of_letters'])
if number_of_letters!=-1:
element_annotation_name = input_dict['element_annotation']
output_annotation_name = input_dict['output_feature']
for doc in adc.documents:
for annotation in doc.get_annotations(element_annotation_name):
if not output_annotation_name in annotation.features:
print input_dict['pos_tagger'],annotation.features
print doc.features
else:
annotation.features[output_annotation_name]=annotation.features[output_annotation_name][0:number_of_letters]
return {'adc': adc }
def pos_tagger_hub(input_dict):
if input_dict['pos_tagger'].__class__.__name__=="LatinoObject": #check if this is a latino object
from tf_latino.latino.library_gen import latino_pos_tag
from workflows.tasks import executeFunction
adc= executeFunction.apply_async([latino_pos_tag,input_dict],queue="windows").wait()['adc'] \
if settings.USE_WINDOWS_QUEUE else latino_pos_tag(input_dict)
else:
adc= universal_sentence_tagger_hub(input_dict)['adc']
number_of_letters=int(input_dict['num_of_letters'])
if number_of_letters!=-1:
element_annotation_name = input_dict['element_annotation']
output_annotation_name = input_dict['output_feature']
for doc in adc.documents:
for annotation in doc.get_annotations(element_annotation_name):
if not output_annotation_name in annotation.features:
print input_dict['pos_tagger'],annotation.features
print doc.features
else:
annotation.features[output_annotation_name]=annotation.features[output_annotation_name][0:number_of_letters]
return {'adc': adc }