def _process_text(parser, session, text, all_names, xform):
""" Low-level utility function to parse text and return the result of
a transformation function (xform) for each sentence.
Set all_names = True to get a comprehensive name register.
Set all_names = False to get a simple name register.
Set all_names = None to get no name register. """
t0 = time.time()
# Demarcate paragraphs in the input
text = mark_paragraphs(text)
# Tokenize the result
token_stream = tokenize(text)
toklist = list(
recognize_entities(token_stream, enclosing_session=session))
t1 = time.time()
pgs, stats = TreeUtility._process_toklist(parser, session, toklist,
xform)
if all_names is None:
register = None
else:
from queries.builtin import create_name_register
register = create_name_register(toklist,
session,
all_names=all_names)
t2 = time.time()
stats["tok_time"] = t1 - t0
stats["parse_time"] = t2 - t1
stats["total_time"] = t2 - t0
return (pgs, stats, register)
def tag_toklist(session, toklist, all_names=False):
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens """
def xform(tokens, tree, err_index):
""" Transformation function that simply returns a list of POS-tagged,
normalized tokens for the sentence """
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
with Fast_Parser(
verbose=False) as parser: # Don't emit diagnostic messages
pgs, stats = TreeUtility._process_toklist(parser, session, toklist,
xform)
from queries.builtin import create_name_register
register = create_name_register(toklist, session, all_names=all_names)
return pgs, stats, register