def getQ(t,s):
subj = None
vb = None
obj = None
G = getGraph(s)
pnodes = gSearchW(G, 'ROOT-0', 'aux') # look for primary node with an aux edge
if t == 0:
if len(pnodes)==0:
pnodes = gSearchW(G, 'ROOT-0', 'cop')
if len(pnodes)==0:
print 'Could not decipher question: ' + api.toString(s)
return None
pnode = pnodes[0]
if 'VB' in s[idx(pnode)]['POS']:
vb = word(pnode)
elif 'NN' in s[idx(pnode)]['POS']:
obj = getPhrase(G,pnode, OBJPE)
for nb in G[pnode]:
w = nb[0]
rel = nb[1]
if rel == 'nsubj':
subj = getPhrase(G,w,GPE)
elif not vb and rel == 'cop':
vb = word(w)
elif not obj and rel in OBJ:
obj = getPhrase(G,w, GPE)
else:
print 'not yet implemented'
return (subj, vb, obj)
def main(fileloc, out):
article = api.parsefile(fileloc)
fout = open(out, 'w')
for s in article:
if not containsPronoun(s):
fout.write(api.toString(s)+"\n")
fout.close()
return
def mostRelevant(q, article, V, N):
dictq = {}
for tok in q:
dictq[tok['lemma']] = tok['POS']
sentencerank = []
for i in range(len(article)):
s = article[i]
sentencerank.append((api.toString(article[i]), cosDist(dictq, s, V, N)))
sentencerank = sorted(sentencerank, key = lambda t: t[1], reverse=True)
return sentencerank
def simScore2(questions, article):
qs = open('semilar/q','w')
sents = open('semilar/s', 'w')
for q in questions:
qs.write(api.toString(q)+"\n")
for s in article:
sents.write(api.toString(s)+"\n")
qs.close()
sents.close()
scores = []
os.chdir('semilar')
rc = subprocess.call('run q s > /dev/null 2>/dev/null', shell=True)
os.chdir('..')
results = open('semilar/out')
for l in results:
toks = l.split('\t')
qindex = int(toks[0])
sindex = int(toks[1])
val = float(toks[2])
scores.append(val)
results.close()
return scores
def mostRelevant(q, article, V, N, sents):
dictq = {}
q = q[1:]
for tok in q:
dictq[tok['lemma']] = tok['POS']
sentencerank = []
simscores = simScore2([q], article)
for i in range(len(article)):
s = article[i]
(score, matched) = cosDist(dictq, s, V, N)
#(simscore, matched2) = simScore(api.toString(q), dictq, s, matched = matched)
simscore = 0
if len(article)==len(simscores):
simscore = simscores[i]
#simscore = 1.0/(1+math.exp(-(simscore-0.5)))
#pdbg(str(score)+","+str(simscore)+"|"+ api.toString(s),t1=simscore+score, t2=2)
score = score + 6*simscore
sentencerank.append((api.toString(article[i]), score, simscore, matched))
sentencerank = sorted(sentencerank, key = lambda t: t[1], reverse=True)
return sentencerank
def getGraph(s):
G = {}
orig = api.toString(s)
dep = api.corenlp.raw_parse(orig)['sentences'][0]['indexeddependencies']
for d in dep:
#rel(x,y) gives the relation of y to x
#interpret it as y is a rel(x,y) of x
rel = d[0] #relationship between words
x = d[1] # the word that y is in relation to
y = d[2] # the word that we want to find the relation of
if not y in G:
G[y] = []
if x in G:
if (y, rel) in G[x]:
continue
else:
G[x].append((y,rel))
else:
G[x] = [(y,rel)]
return G
