def infer_derivative(sense, main_sense_of_entry):
equiv = None
if (main_sense_of_entry is not None and
not main_sense_of_entry.is_affix() and
main_sense_of_entry.thesclass is not None):
# Take precedent from the main sense of the parent entry
equiv = tdb.equivalent_class(main_sense_of_entry.thesclass, sense.wordclass)
if equiv is not None:
equiv.reason_text = 'Parallel to "%s"' % main_sense_of_entry.lemma
equiv.reason_code = 'driv'
if equiv is None:
# Take precedent from other sibling derivatives
candidates = tdb.ranked_search(refentry=sense.entry_id,
thes_linked=True, currentOnly=True)
candidates = [c for c in candidates if c.is_derivative() and
not ' ' in c.lemma and
c.superclass() is not None]
if candidates:
j = defaultdict(int)
for c in candidates:
j[c.superclass()] += 1
parents = [p for p, num in j.items() if num == max(j.values())]
# If there's more than one possible parent, pick the one with
# the largest branch
parents.sort(key=lambda p: p.branch_size, reverse=True)
equiv = tdb.child_wordclass_branch(parents[0], sense.wordclass)
if equiv is None:
equiv = parents[0]
if equiv is not None:
equiv.reason_text = 'Parallel to %s' % ', '.join(
['"%s"' % (c.lemma,) for c in candidates if c.lemma])
equiv.reason_code = 'driv'
return equiv
def branches_from_xrefs(self, sense):
branches = set()
for xr in sense.cross_references:
instances = tdb.ranked_search(refentry=xr.refentry, refid=xr.refid)
local_branches = _parse_instances(instances)
branches = branches | local_branches
return [b.id for b in branches]
def finalize():
"""
Use the 'manual' file to override where necessary
"""
for wordclass in WORDCLASSES:
lemmas = {}
infile1 = os.path.join(DIRECTORY, '%s_compounds.csv' % wordclass)
infile2 = os.path.join(DIRECTORY, '%s_manual.csv' % wordclass)
with open(infile1, 'r') as filehandle:
csvreader = csv.reader(filehandle)
for row in csvreader:
lemmas[row[0]] = int(row[1])
# Do the manual file second, so that it overrides the
# automatically-generated file
with open(infile2, 'r') as filehandle:
csvreader = csv.reader(filehandle)
for row in csvreader:
lemmas[row[0]] = int(row[1])
output = []
for lemma, class_id in lemmas.items():
# Retrieve the branch that the majority of compounds are on
compound_branch = tdb.get_thesclass(class_id)
# Get the highest-rated senses for the lemma
ranked_senses = tdb.ranked_search(lemma=lemma, wordclass=wordclass)
if ranked_senses:
max_rating = ranked_senses[0].rating()
ranked_senses = [s for s in ranked_senses if
max_rating > 0 and
s.rating() > max_rating * 0.3]
# Try filtering to just those senses that are on
# the same branch as the compounds
ranked_filtered = [s for i, s in enumerate(ranked_senses) if
(i == 0 and s.thesclass is None) or
s.is_descendant_of(compound_branch)]
# ... or else stick with original ranking
if not ranked_filtered:
ranked_filtered = ranked_senses
if ranked_filtered:
output.append(ranked_filtered[0])
outfile = os.path.join(DIRECTORY, '%s.csv' % wordclass)
output.sort(key=lambda s: s.lemma)
with open(outfile, 'w') as filehandle:
csvwriter = csv.writer(filehandle)
for s in output:
row = (s.lemma, s.refentry, s.refid,
s.entry_size, s.breadcrumb())
csvwriter.writerow(row)
def compound_derivative(sense):
if sense.last_element() is None:
return None, None
thesclass = None
for ending, wordclasses, replacement in derivation_forms:
if (sense.wordclass in wordclasses and
sense.last_element().endswith(ending) and
len(sense.last_element()) > len(ending) + 2):
# Figure out what the base form would look like,
# if the lemma *is* a derivative
# - Strip off the ending, then add the replacement ending
# (which is usually a null string).
hypothetical_base =\
sense.lemma[0:len(sense.lemma)-len(ending)] + replacement
# Test if the hypothetical base form exists, and if so
# find out how it is classified
base_classifications = tdb.ranked_search(lemma=hypothetical_base,
current_only=True)
if (tdb.distinct_senses(base_classifications) == 1 and
base_classifications[0].thesclass is not None):
thesclass = base_classifications[0].thesclass
break
if thesclass is not None:
# Don't risk things like 'yellow-bellied' - these are
# likely to be transparent, not a derivative of e.g.
# 'yellow-belly', so should go after existing guesses
if ending in ('ed', 'ied') and sense.first_element().lower() in colours:
position = 'last'
else:
position = 'first'
return base_classifications[0].thesclass, position
else:
return None, None
def ranked_sense_summary(**kwargs):
"""
Return a summary of the ranked_search() results, reduced to a
given thesaurus level (defaults to level=3)
Returns a ranked list of result objects, each with the following
attributes:
* parent: level-3 parent class;
* classes: ranked list of thesaurus classes which map to the parent;
* summed_rating: sum of c.rating() values for each c in classes;
* probability: probability of this row (based on summed rating).
Keyword arguments:
* level (int): the parent thesaurus level which will be returned
* omit_null (True/False): if True, any senses which don't have
a classification will be ignored
... plus all the usual optional keyword arguments passed on to
tdb.search() (lemma, wordclass, refentry, refid, etc.).
"""
level = kwargs.get('level', 3)
lemma = kwargs.get('lemma')
wordclass = kwargs.get('wordclass')
omit_null = kwargs.get('omit_null', False)
if lemma is not None:
main_sense = main_sense_finder.main_sense(lemma=lemma,
wordclass=wordclass,
listed_only=True)
if main_sense is not None:
kwargs['promote'] = main_sense.refid
candidates = tdb.ranked_search(**kwargs)
if omit_null:
candidates = [c for c in candidates if c.thesclass is not None]
# Give each thesclass a probability value (as a ratio of the sum
# of all senses' ratings)
total = sum([c.rating() for c in candidates])
if total <= 0:
total = 1
summary = {}
for c in candidates:
if c.thesclass is None or c.thesclass.ancestor(level=level) is None:
ancestor = None
identifier = 0
else:
ancestor = c.thesclass.ancestor(level=level)
identifier = ancestor.id
if not identifier in summary:
summary[identifier] = ResultRow(ancestor)
summary[identifier].append(c)
# Convert to a list
summary = list(summary.values())
# Add a probability score (0 < p < 1) to each row
[row.set_probability(total) for row in summary]
# Sort by probability
summary.sort(key=lambda r: r.probability, reverse=True)
return summary
def match_single_synonym(sense):
# Drop out any highly polysemous synonyms
synonyms = []
for syn in sense.synonyms:
instances = tdb.search(lemma=syn,
wordclass=sense.wordclass,
current_only=True)
if tdb.distinct_senses(instances) < 20:
synonyms.append(syn)
if not synonyms:
return None, None
match = None
matching_synonym = None
# If the sense can be restricted by subject area, try to find a match for
# *any* synonym (even if there's only one)
if not match and synonyms and sense.subjects:
candidates = []
for syn in synonyms:
candidates.extend(tdb.ranked_search(lemma=syn,
wordclass=sense.wordclass,
subjects=sense.subjects,
current_only=True))
if candidates and candidates[0].thesclass is not None:
match = candidates[0].thesclass
matching_synonym = candidates[0].lemma
# If the sense is an interjection, try to find a match for
# *any* synonym (even if there's only one) - since interjection
# synonyms are more reliable and less ambiguous
if not match and synonyms and sense.wordclass == 'UH':
candidates = []
for syn in synonyms:
candidates.extend(tdb.ranked_search(lemma=syn,
wordclass='UH',
current_only=True))
if candidates and candidates[0].thesclass is not None:
match = candidates[0].thesclass
matching_synonym = candidates[0].lemma
# If any of the synonyms are single-sense (or nearly single-sense),
# then we assume that that is the correct sense
if not match:
candidates = []
for syn in synonyms:
syn_senses = tdb.ranked_search(lemma=syn,
wordclass=sense.wordclass,
current_only=True)
if (syn_senses and
(tdb.distinct_senses(syn_senses) == 1 or
(tdb.distinct_senses(syn_senses) <= 3 and
len(synonyms) == 1))):
candidates.append(syn_senses[0])
for c in candidates:
if c.thesclass is not None:
match = c.thesclass
matching_synonym = c.lemma
break
# If the sense can be restricted by Bayes classification(s), try to
# find a match for *any* synonym (even if there's only one)
if not match and synonyms and sense.bayes.is_usable():
candidates = []
for syn in synonyms:
candidates.extend(tdb.ranked_search(lemma=syn,
wordclass=sense.wordclass,
branches=sense.bayes.ids(),
current_only=True))
if candidates and candidates[0].thesclass is not None:
match = candidates[0].thesclass
matching_synonym = candidates[0].lemma
return match, matching_synonym
def find_branch_from_superordinate(self, sense):
"""Classify by finding the main or only sense of the superordinate
"""
if (sense.wordclass not in ('NN', 'JJ') or
not sense.superordinate or
len(sense.superordinate) < 3 or
sense.superordinate in GENERICS):
return None
target_sense = None
# If the superordinate is (more or less) single-sense, we assume that
# sense to be the correct one
candidates = tdb.ranked_search(
lemma=sense.superordinate,
wordclass='NN',
current_only=True)
if candidates and tdb.distinct_senses(candidates) <= 2:
target_sense = candidates[0]
# Otherwise, narrow by Bayes classification
if target_sense is None and sense.bayes.confidence() >= 8:
target_sense = tdb.highest_ranked(
lemma=sense.superordinate,
wordclass='NN',
branches=sense.bayes_based_classifications,
current_only=True)
# Otherwise, narrow by branches based on subject labels
if target_sense is None and sense.label_based_classifications:
target_sense = tdb.highest_ranked(
lemma=sense.superordinate,
wordclass='NN',
branches=sense.label_based_classifications,
current_only=True)
# Otherwise, narrow by branches based on cross-references
if target_sense is None and sense.xref_branches:
target_sense = tdb.highest_ranked(
lemma=sense.superordinate,
wordclass='NN',
branches=sense.xref_branches,
current_only=True)
# Last gasp: If the gloss consists more or less *only* of the
# superordinate (e.g. 'an abbey'), then it should be adequate to
# just use the main sense of the superordinate, even if it's
# multi-sense.
# But don't risk this is there are cross-references or subject
# labels which might suggest a more specific use
if (target_sense is None and not sense.subjects and
not sense.xref_branches and sense.gloss is not None):
g = re.sub(r'^(a|an|the) ', '', sense.gloss.lower())
if g == sense.superordinate:
target_sense = MAIN_SENSE_FINDER.main_sense(
lemma=sense.superordinate, wordclass='NN')
# Otherwise, narrow by Bayes classification
if target_sense is None and sense.bayes.is_usable():
target_sense = tdb.highest_ranked(
lemma=sense.superordinate,
wordclass='NN',
branches=sense.bayes_based_classifications,
current_only=True)
if target_sense is not None and target_sense.thesclass is not None:
match = target_sense.thesclass
if sense.wordclass == 'JJ':
match = tdb.equivalent_class(match, 'JJ')
return match
else:
return None
