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bibclassify_keyword_analyzer.py
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bibclassify_keyword_analyzer.py
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# -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2008, 2009, 2010, 2011,  CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software Foundation; either version 2 of the
## License, or (at your option) any later version.
##
## Invenio is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with Invenio; if not, write to the Free Software Foundation, Inc.,
## 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
"""
BibClassify keyword analyser.
This module contains methods to extract keywords from texts. It provides 3
different methods for 3 different types of keywords: single keywords, composite
keywords and author keywords.
"""
import re
import sys
import time
try:
from bibclassify_config import CFG_BIBCLASSIFY_VALID_SEPARATORS, \
CFG_BIBCLASSIFY_AUTHOR_KW_START, \
CFG_BIBCLASSIFY_AUTHOR_KW_END, \
CFG_BIBCLASSIFY_AUTHOR_KW_SEPARATION
from bibclassify_utils import write_message
except ImportError, err:
print >> sys.stderr, "Error: %s" % err
sys.exit(1)
# Retrieve the custom configuration if it exists.
try:
from bibclassify_config_local import *
except ImportError:
# No local configuration was found.
pass
_MAXIMUM_SEPARATOR_LENGTH = max([len(_separator)
for _separator in CFG_BIBCLASSIFY_VALID_SEPARATORS])
def get_single_keywords(skw_db, fulltext, verbose=True):
"""Returns a dictionary of single keywords bound with the positions
of the matches in the fulltext.
Format of the output dictionary is (single keyword: positions)."""
timer_start = time.clock()
# Matched span -> single keyword
records = []
for single_keyword in skw_db:
for regex in single_keyword.regex:
for match in regex.finditer(fulltext):
# Modify the right index to put it on the last letter
# of the word.
span = (match.span()[0], match.span()[1] - 1)
# Remove the previous records contained by this span
records = [record for record in records
if not _contains_span(span, record[0])]
add = True
for previous_record in records:
if ((span, single_keyword) == previous_record or
_contains_span(previous_record[0], span)):
# Match is contained by a previous match.
add = False
break
if add:
records.append((span, single_keyword))
# List of single_keywords: {spans: single keyword}
single_keywords = {}
for span, single_keyword in records:
single_keywords.setdefault(single_keyword, []).append(span)
if verbose:
write_message("INFO: Matching single keywords... %d keywords found "
"in %.1f sec." % (len(single_keywords), time.clock() - timer_start),
stream=sys.stderr, verbose=3)
return single_keywords
def get_composite_keywords(ckw_db, fulltext, skw_spans, verbose=True):
"""Returns a list of composite keywords bound with the number of
occurrences found in the text string.
Format of the output list is (composite keyword, count, component counts)."""
timer_start = time.clock()
# Build the list of composite candidates
ckw_list = []
skw_as_components = []
for composite_keyword in ckw_db:
# Counters for the composite keyword. First count is for the
# number of occurrences in the whole document and second count
# is for the human defined keywords.
ckw_count = 0
matched_spans = []
# Check the alternative labels.
for regex in composite_keyword.regex:
for match in regex.finditer(fulltext):
span = list(match.span())
span[1] -= 1
span = tuple(span)
if not span in matched_spans:
ckw_count += 1
matched_spans.append(span)
# Get the single keywords locations.
try:
components = composite_keyword.compositeof
except AttributeError:
print >> sys.stderr, ("Cached ontology is corrupted. Please "
"remove the cached ontology in your temporary file.")
sys.exit(1)
try:
spans = [skw_spans[component] for component in components]
except KeyError:
# The keyword components are not to be found in the text.
# This is not a dramatic exception and we can safely ignore
# it.
pass
else:
ckw_spans = []
for index in range(len(spans) - 1):
if ckw_spans:
previous_spans = ckw_spans
else:
previous_spans = spans[index]
ckw_spans = []
for new_span in [(span0, span1) for span0 in previous_spans
for span1 in spans[index + 1]]:
span = _get_ckw_span(fulltext, new_span)
if span is not None:
ckw_spans.append(span)
for span in [span for span in ckw_spans
if not span in matched_spans]:
ckw_count += 1
matched_spans.append(span)
if ckw_count:
# Gather the component counts.
component_counts = []
for component in components:
skw_as_components.append(component)
# Get the single keyword count.
try:
component_counts.append(len(skw_spans[component]))
except KeyError:
component_counts.append(0)
# Store the composite keyword
ckw_list.append((composite_keyword, ckw_count, component_counts))
# Remove the single keywords that appear as components from the list
# of single keywords.
for skw in skw_as_components:
try:
del skw_spans[skw]
except KeyError:
pass
if verbose:
write_message("INFO: Matching composite keywords... %d keywords found "
"in %.1f sec." % (len(ckw_list), time.clock() - timer_start),
stream=sys.stderr, verbose=3)
return ckw_list
def get_author_keywords(skw_db, ckw_db, fulltext):
"""Finds out human defined keyowrds in a text string. Searches for
the string "Keywords:" and its declinations and matches the
following words."""
timer_start = time.clock()
split_string = CFG_BIBCLASSIFY_AUTHOR_KW_START.split(fulltext, 1)
if len(split_string) == 1:
write_message("INFO: Matching author keywords... No keywords found.",
stream=sys.stderr, verbose=3)
return None
kw_string = split_string[1]
for regex in CFG_BIBCLASSIFY_AUTHOR_KW_END:
parts = regex.split(kw_string, 1)
kw_string = parts[0]
# We separate the keywords.
author_keywords = CFG_BIBCLASSIFY_AUTHOR_KW_SEPARATION.split(kw_string)
write_message("INFO: Matching author keywords... %d keywords found in "
"%.1f sec." % (len(author_keywords), time.clock() - timer_start),
stream=sys.stderr, verbose=3)
out = {}
for kw in author_keywords:
# If the author keyword is an acronym with capital letters
# separated by points, remove the points.
if re.match('([A-Z].)+$', kw):
kw = kw.replace('.', '')
# First try with the keyword as such, then lower it.
kw_with_spaces = ' %s ' % kw
matching_skw = get_single_keywords(skw_db, kw_with_spaces,
verbose=False)
matching_ckw = get_composite_keywords(ckw_db, kw_with_spaces,
matching_skw, verbose=False)
if matching_skw or matching_ckw:
out[kw] = (matching_skw, matching_ckw)
continue
lowkw = kw.lower()
matching_skw = get_single_keywords(skw_db, ' %s ' % lowkw, verbose=False)
matching_ckw = get_composite_keywords(ckw_db, ' %s ' % lowkw,
matching_skw, verbose=False)
out[kw] = (matching_skw, matching_ckw)
return out
def _get_ckw_span(fulltext, spans):
"""Returns the span of the composite keyword if it is valid. Returns
None otherwise."""
if spans[0] < spans[1]:
words = (spans[0], spans[1])
dist = spans[1][0] - spans[0][1]
else:
words = (spans[1], spans[0])
dist = spans[0][0] - spans[1][1]
if dist == 0:
# Two keywords are adjacent. We have a match.
return (min(words[0] + words[1]), max(words[0] + words[1]))
elif dist <= _MAXIMUM_SEPARATOR_LENGTH:
separator = fulltext[words[0][1]:words[1][0] + 1]
# Check the separator.
if separator.strip() in CFG_BIBCLASSIFY_VALID_SEPARATORS:
return (min(words[0] + words[1]), max(words[0] + words[1]))
# There is no inclusion.
return None
def _contains_span(span0, span1):
"""Return true if span0 contains span1, False otherwise."""
if (span0 == span1 or
span0[0] > span1[0] or
span0[1] < span1[1]):
return False
return True

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