def get_keywords_from_text(text_lines, taxonomy=None, output_mode="text",
output_limit=CFG_BIBCLASSIFY_DEFAULT_OUTPUT_NUMBER, spires=False,
match_mode="full", no_cache=False, with_author_keywords=False,
rebuild_cache=False, only_core_tags=False):
"""Returns a formatted string containing the keywords for a single
document."""
global _SKWS
global _CKWS
if not _SKWS:
if taxonomy is not None:
_SKWS, _CKWS = get_regular_expressions(taxonomy,
rebuild=rebuild_cache, no_cache=no_cache)
else:
write_message("ERROR: Please specify an ontology in order to "
"extract keywords.", stream=sys.stderr, verbose=1)
text_lines = cut_references(text_lines)
fulltext = normalize_fulltext("\n".join(text_lines))
author_keywords = None
if with_author_keywords:
author_keywords = get_author_keywords(_SKWS, _CKWS, fulltext)
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
single_keywords = get_single_keywords(_SKWS, fulltext)
composite_keywords = get_composite_keywords(_CKWS, fulltext,
single_keywords)
return _get_keywords_output(single_keywords, composite_keywords, taxonomy,
author_keywords, output_mode, output_limit, spires, only_core_tags)
def get_keywords_from_local_file(local_file, taxonomy, rebuild_cache=False,
match_mode="full", no_cache=False, with_author_keywords=False):
text_lines = text_lines_from_local_file(local_file)
global _SKWS
global _CKWS
if not _SKWS:
if taxonomy is not None:
_SKWS, _CKWS = get_regular_expressions(taxonomy,
rebuild=rebuild_cache, no_cache=no_cache)
else:
write_message("ERROR: Please specify an ontology in order to "
"extract keywords.", stream=sys.stderr, verbose=1)
text_lines = cut_references(text_lines)
fulltext = normalize_fulltext("\n".join(text_lines))
author_keywords = None
if with_author_keywords:
author_keywords = get_author_keywords(_SKWS, _CKWS, fulltext)
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
single_keywords = get_single_keywords(_SKWS, fulltext)
composite_keywords = get_composite_keywords(_CKWS, fulltext,
single_keywords)
return (single_keywords, composite_keywords)
def get_keywords_from_text(text_lines, taxonomy=None, output_mode="text",
output_limit=CFG_BIBCLASSIFY_DEFAULT_OUTPUT_NUMBER, spires=False,
match_mode="full", no_cache=False, with_author_keywords=False,
rebuild_cache=False, only_core_tags=False):
"""Returns a formatted string containing the keywords for a single
document."""
global _SKWS
global _CKWS
if not _SKWS:
if taxonomy is not None:
_SKWS, _CKWS = get_regular_expressions(taxonomy,
rebuild=rebuild_cache, no_cache=no_cache)
else:
write_message("ERROR: Please specify an ontology in order to "
"extract keywords.", stream=sys.stderr, verbose=1)
text_lines = cut_references(text_lines)
fulltext = normalize_fulltext("\n".join(text_lines))
author_keywords = None
if with_author_keywords:
author_keywords = get_author_keywords(_SKWS, _CKWS, fulltext)
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
single_keywords = get_single_keywords(_SKWS, fulltext)
composite_keywords = get_composite_keywords(_CKWS, fulltext,
single_keywords)
return _get_keywords_output(single_keywords, composite_keywords, taxonomy,
author_keywords, output_mode, output_limit, spires, only_core_tags)
def get_keywords_from_local_file(local_file, taxonomy, rebuild_cache=False,
match_mode="full", no_cache=False, with_author_keywords=False):
text_lines = text_lines_from_local_file(local_file)
global _SKWS
global _CKWS
if not _SKWS:
if taxonomy is not None:
_SKWS, _CKWS = get_regular_expressions(taxonomy,
rebuild=rebuild_cache, no_cache=no_cache)
else:
write_message("ERROR: Please specify an ontology in order to "
"extract keywords.", stream=sys.stderr, verbose=1)
text_lines = cut_references(text_lines)
fulltext = normalize_fulltext("\n".join(text_lines))
author_keywords = None
if with_author_keywords:
author_keywords = get_author_keywords(_SKWS, _CKWS, fulltext)
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
single_keywords = get_single_keywords(_SKWS, fulltext)
composite_keywords = get_composite_keywords(_CKWS, fulltext,
single_keywords)
return (single_keywords, composite_keywords)
def get_keywords_from_text(
text_lines,
taxonomy_name,
output_mode="text",
output_limit=bconfig.CFG_BIBCLASSIFY_DEFAULT_OUTPUT_NUMBER,
spires=False,
match_mode="full",
no_cache=False,
with_author_keywords=False,
rebuild_cache=False,
only_core_tags=False,
extract_acronyms=False,
**kwargs):
"""Extracts keywords from the list of strings
@var text_lines: list of strings (will be normalized before being
joined into one string)
@keyword taxonomy_name: string, name of the taxonomy_name
@keyword output_mode: string - text|html|marcxml|raw
@keyword output_limit: int
@keyword spires: boolean, if True marcxml output reflect spires
codes
@keyword match_mode: str - partial|full; in partial mode only
beginning of the fulltext is searched
@keyword no_cache: boolean, means loaded definitions will not be saved
@keyword with_author_keywords: boolean, extract keywords from the
pdfs
@keyword rebuild_cache: boolean
@keyword only_core_tags: boolean
@return: if output_mode=raw, it will return
(single_keywords, composite_keywords, author_keywords, acronyms)
for other output modes it returns formatted string
"""
start_time = time.time()
cache = reader.get_cache(taxonomy_name)
if not cache:
reader.set_cache(
taxonomy_name,
reader.get_regular_expressions(taxonomy_name,
rebuild=rebuild_cache,
no_cache=no_cache))
cache = reader.get_cache(taxonomy_name)
_skw = cache[0]
_ckw = cache[1]
text_lines = normalizer.cut_references(text_lines)
fulltext = normalizer.normalize_fulltext("\n".join(text_lines))
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
author_keywords = None
if with_author_keywords:
author_keywords = extract_author_keywords(_skw, _ckw, fulltext)
acronyms = {}
if extract_acronyms:
acronyms = extract_abbreviations(fulltext)
single_keywords = extract_single_keywords(_skw, fulltext)
composite_keywords = extract_composite_keywords(_ckw, fulltext,
single_keywords)
if only_core_tags:
single_keywords = clean_before_output(
_filter_core_keywors(single_keywords))
composite_keywords = _filter_core_keywors(composite_keywords)
else:
# Filter out the "nonstandalone" keywords
single_keywords = clean_before_output(single_keywords)
log.info('Keywords generated in: %.1f sec' % (time.time() - start_time))
if output_mode == "raw":
if output_limit:
return (
_kw(_sort_kw_matches(single_keywords, output_limit)),
_kw(_sort_kw_matches(composite_keywords, output_limit)),
author_keywords, # this we don't limit (?)
_kw(_sort_kw_matches(acronyms, output_limit)))
else:
return (single_keywords, composite_keywords, author_keywords,
acronyms)
else:
return get_keywords_output(single_keywords, composite_keywords,
taxonomy_name, author_keywords, acronyms,
output_mode, output_limit, spires,
only_core_tags)
def get_keywords_from_text(text_lines, taxonomy_name, output_mode="text",
output_limit=bconfig.CFG_BIBCLASSIFY_DEFAULT_OUTPUT_NUMBER, spires=False,
match_mode="full", no_cache=False, with_author_keywords=False,
rebuild_cache=False, only_core_tags=False, extract_acronyms=False,
**kwargs):
"""Extracts keywords from the list of strings
@var text_lines: list of strings (will be normalized before being
joined into one string)
@keyword taxonomy_name: string, name of the taxonomy_name
@keyword output_mode: string - text|html|marcxml|raw
@keyword output_limit: int
@keyword spires: boolean, if True marcxml output reflect spires
codes
@keyword match_mode: str - partial|full; in partial mode only
beginning of the fulltext is searched
@keyword no_cache: boolean, means loaded definitions will not be saved
@keyword with_author_keywords: boolean, extract keywords from the
pdfs
@keyword rebuild_cache: boolean
@keyword only_core_tags: boolean
@return: if output_mode=raw, it will return
(single_keywords, composite_keywords, author_keywords, acronyms)
for other output modes it returns formatted string
"""
cache = reader.get_cache(taxonomy_name)
if not cache:
reader.set_cache(taxonomy_name, reader.get_regular_expressions(taxonomy_name,
rebuild=rebuild_cache, no_cache=no_cache))
cache = reader.get_cache(taxonomy_name)
_skw = cache[0]
_ckw = cache[1]
text_lines = normalizer.cut_references(text_lines)
fulltext = normalizer.normalize_fulltext("\n".join(text_lines))
author_keywords = None
if with_author_keywords:
author_keywords = extract_author_keywords(_skw, _ckw, fulltext)
if match_mode == "partial":
fulltext = _get_partial_text(fulltext)
single_keywords = extract_single_keywords(_skw, fulltext)
composite_keywords = extract_composite_keywords(_ckw, fulltext, single_keywords)
acronyms = {}
if extract_acronyms:
acronyms = extract_abbreviations(fulltext)
if output_mode == "raw":
if output_limit:
return (_kw(_sort_kw_matches(single_keywords, output_limit)),
_kw(_sort_kw_matches(composite_keywords, output_limit)),
author_keywords, # this we don't limit (?)
_kw(_sort_kw_matches(acronyms, output_limit)))
else:
return (single_keywords, composite_keywords, author_keywords, acronyms)
else:
return _get_keywords_output(single_keywords, composite_keywords, taxonomy_name,
author_keywords, acronyms, output_mode, output_limit,
spires, only_core_tags)
