def main(sentenceId, jsonFile, tokens, ww, wTags, depParse, inAMR, alignment, completed):
amr = inAMR
triples = set() # to add to the AMR
props = pipeline.loadNProp(jsonFile)
predheads = {} # map head index to nominal predicate variable (not reflected in the alignment)
# add all predicates first, so the roleset properly goes into the AMR
for prop in props:
baseform, roleset = prop["baseform"], prop["frame"]
if not config.fullNombank and not verbalize.nompred2verbpred(roleset):
continue # TODO: maybe add just the pred stem & non-core args that map to AMR role names?
preds = {tuple(arg) for arg in prop["args"] if arg[0]=='rel'}
assert len(preds)==1
pred = next(iter(preds))
assert pred[2]==pred[3] # multiword predicates?
ph = pred[2] # predicate head
#px = alignment[:ph] # instead of aligning noun predicate to noun in the sentence, introduce the noun predicate separately (so the plain noun concept can be its argument)
px = predheads.get(ph)
predconcept = pipeline.token2concept(roleset.replace('.','-n-'))
if not (px or px==0):
px = new_concept(predconcept, amr) # no alignment here - instead use 'predheads'
#print('###','newconcept',px,'/',predconcept)
px0 = alignment[:ph]
if not (px0 or px0==0):
px0 = new_concept_from_token(amr, alignment, ph, depParse, wTags)
triples.add((str(px0), '-PRED', str(px)))
#if len(prop["args"])==1 or (prop["args"][0][0] in ['Support','rel'] and prop["args"][1][0] in ['Support','rel']):
# triples.add((str(px), '-DUMMY', ''))
predheads[ph] = px
else: # predicate already a concept in the AMR (e.g. inserted by the 'nouns' module)
amr.node_to_concepts[str(px)] = predconcept # change the name of the concept
completed[0][ph] = True
# now handle arguments
for prop in props:
baseform, roleset = prop["baseform"], prop["frame"]
pred = [arg for arg in prop["args"] if arg[0]=='rel'][0]
ph = pred[2] # predicate head
#px = alignment[:ph]
if ph not in predheads:
continue
px = predheads[ph]
for rel,treenode,i,j,yieldS in prop["args"]:
if i is None or j is None: continue # TODO: special PropBank cases that need further work
if rel in ['rel', 'Support']: continue
assert rel[:3]=='ARG'
h = choose_head(range(i,j+1), depParse)
if h is None: continue # TODO: improve coverage of complex spans
# handle general proposition arguments
if str(alignment[:h]) in amr.node_to_concepts:
rel, amr.node_to_concepts[str(alignment[:h])] = common_arg(rel, amr.get_concept(str(alignment[:h])))
else:
drels = [dep["rel"] for dep in depParse[h]]
rel = common_arg(rel, drels=drels)
if isinstance(rel,tuple):
rel, val = rel
assert isinstance(val,Atom)
triples.add((str(px), rel, val))
else:
x = amrget(amr, alignment, h, depParse, wTags)
triples.add((str(px), rel, str(x)))
#print('###',px,rel,x)
completed[0][h] = True
# if SRL argument link corresponds to a dependency edge, mark that edge as complete
if (ph,h) in completed[1]:
completed[1][(ph,h)] = True
#print('completed ',(ph,h))
if (h,ph) in completed[1]: # also for reverse direction
completed[1][(h,ph)] = True
#print('completed ',(ph,h))
#print(triples)
amr = new_amr_from_old(amr, new_triples=list(triples))
return depParse, amr, alignment, completed
def main(sentenceId, jsonFile, tokens, ww, wTags, depParse, inAMR, alignment, completed):
amr = inAMR
new_triples = set()
nNewTrip = 0
time_expressions = pipeline.loadTimex(jsonFile)
for tid, start, end, raw_timex in time_expressions:
t = Timex3Entity(ElementTree.fromstring(raw_timex))
h = choose_head(range(start,end+1), depParse)
mc = new_concept_from_token(amr, alignment, h, depParse, wTags, concept=pipeline.token2concept(t.main_concept))
if t.wrapper != None:
alignment.unlink(mc, h)
wc = new_concept_from_token(amr, alignment, h, depParse, wTags, concept=pipeline.token2concept(t.wrapper)+'-'+t.type)
new_triples.add((str(wc), 'op1', str(mc)))
else:
amr.node_to_concepts[str(mc)] += '-'+t.type
if 'weekday' in t.date_entity:
wd = int(t.date_entity['weekday'])
wd_name = weekdays[wd] # e.g. 'friday'
x = new_concept(pipeline.token2concept(wd_name), amr)
new_triples.add((str(mc), 'weekday', str(x)))
if 'dayperiod' in t.date_entity:
dp = t.date_entity['dayperiod']
dp_name = dayperiods[dp] # e.g. 'afternoon'
x = new_concept(pipeline.token2concept(dp_name), amr)
new_triples.add((str(mc), 'dayperiod', str(x)))
#print('####', t.date_entity)
for k, v in t.date_entity.iteritems():
if k in ['weekday','dayperiod']: continue # handled above
if isinstance(v,basestring):
v = pipeline.token2concept(str(v))
x = new_concept(v, amr)
x = str(x)
else: # leave literal numeric values alone
#print(amr.triples(instances=False))
x = v
new_triples.add((str(mc), k, x))
for i in range(start, end+1): # for now mark everything as completed
completed[0][i] = True
for i,j in completed[1]:
if i >= start and i <= end and j >= start and j <= end:
completed[1][(i,j)] = True
try:
assert t.main_concept and (t.main_concept not in ['date-entity','temporal-quantity'] or len(new_triples)>nNewTrip)
except AssertionError:
if config.verbose or config.warn: print('Warning: Unhandled time expression', file=sys.stderr)
nNewTrip = len(new_triples)
#print(list(new_triples))
amr = new_amr_from_old(amr, new_triples=list(new_triples))
# TODO: mark all internal dependencies as completed?
return depParse, amr, alignment, completed
def main(sentenceId, jsonFile, tokens, ww, wTags, depParse, inAMR, alignment, completed):
amr = inAMR
triples = set() # to add to the AMR
entities = pipeline.loadBBN(jsonFile)
for i,j,name,coarse,fine,raw in entities:
if raw.startswith('<TIMEX'): continue # use the timex module (sutime output) instead
h = choose_head(range(i,j+1), depParse,
fallback=lambda frontier: max(frontier) if len(frontier)==2 and ww[min(frontier)]=='than' else False)
# ^ dirty hack: in 'more than 3 times' (wsj_0003.12), [more than 3] is a value expression
# but 'than' and '3' both attach to 'times' in the dependency parse.
#print((i,j),name,h,depParse[h+1]['dep'], file=sys.stderr)
x = alignment[:h] # index of variable associated with i's head, if any
if raw.startswith('<NUMEX'):
if coarse in ['MONEY','CARDINAL','PERCENT']:
# get normalized value from Stanford tools
v = wTags[h]["NormalizedNamedEntityTag"]
wrapper = None
if v[0] in '<>~':
if len(v)==1:
print('Warning: Unexpected NormalizedNamedEntityTag:',v,'for',raw, file=sys.stderr)
else:
if v[1]=='=':
reln = v[:2]
v = v[2:]
else:
reln = v[0]
v = v[1:]
concept = {'<': 'less-than', '>': 'more-than', '<=': 'no-more-than', '>=': 'at-least', '~': 'about'}[reln]
wrapper = new_concept_from_token(amr, alignment, h, depParse, wTags, concept=concept)
if coarse=='MONEY':
m = re.match(r'^([\$¥£])(\d+\.\d+(E-?\d+)?)$', v)
if not m:
assert False,v
u = m.group(1)
v = m.group(2)
elif coarse=='PERCENT':
m = re.match(r'^%(\d+\.\d+(E-?\d+)?)$', v)
if not m:
assert False,v
v = m.group(1)
try:
v = float(v)
if str(v).endswith('.0'):
v = int(v)
except ValueError:
pass
if (wrapper is None or coarse=='MONEY') and not (x or x==0): # need a new variable
kind = {'MONEY': 'monetary-quantity', 'PERCENT': 'percentage-entity'}.get(coarse, coarse.upper())
if wrapper is None: # if there is a wrapper concept (e.g. 'more-than'), it is aligned, so don't provide an alignment for x
x = new_concept_from_token(amr, alignment, h, depParse, wTags, concept=kind)
else:
x = new_concept(kind, amr)
if (x or x==0):
triples.add((str(x), 'value' if coarse=='PERCENT' else 'quant', v))
if wrapper is not None:
triples.add((str(wrapper), 'op1', str(x)))
elif wrapper is not None:
triples.add((str(wrapper), 'op1', v)) # e.g. more-than :op1 41
if coarse=='MONEY':
y = new_concept({'$': 'dollar', '¥': 'yen', '£': 'pound'}[u.encode('utf-8')], amr)
triples.add((str(x), 'unit', str(y)))
elif coarse=='ORDINAL':
pass # skip--no special treatment in AMR guidelines, though the normalized value could be used
else:
assert False,(i,j,raw)
elif coarse.endswith('_DESC'):
# make the phrase head word the AMR head concept
# (could be a multiword term, like Trade Representative)
if not (x or x==0): # need a new variable
x = new_concept_from_token(amr, alignment, h, depParse, wTags)
triples.add((str(x), '-DUMMY', '')) # ensure the concept participates in some triple so it is printed
else:
if coarse.lower()=='person' and i>0 and ww[i-1] and ww[i-1].lower() in ['mr','mr.','mister','master','sir','mrs','mrs.','miss']:
# Extend the NE to include formal titles that do not get concepts
name = ww[i-1]+' '+name
i -= 1
if not (x or x==0): # need a new variable
ne_class = fine.lower().replace('other','') or coarse.lower()
concept, amr_name = amrify(ne_class, name)
x = new_concept_from_token(amr, alignment, h, depParse, wTags,
concept=pipeline.token2concept(concept)+'-FALLBACK')
# -FALLBACK indicates extra information not in the sentence (NE class)
n = new_concept('name', amr)
triples.add((str(x), 'name', str(n)))
for iw,w in enumerate(amr_name.split()):
triples.add((str(n), 'op'+str(iw+1), '"'+w+'"'))
for k in range(i,j+1):
assert not completed[0][k]
completed[0][k] = True
#print('completed token',k)
if k!=h:
for link in parent_edges(depParse[k]):
completed[1][link] = True # we don't need to attach non-head parts of names anywhere else
amr = new_amr_from_old(amr, new_triples=list(triples))
return depParse, amr, alignment, completed