def inputsentence_analysis(inputsentence):
post_data = "sentence=" + inputsentence
url = urllib2.urlopen("http://barbar.cs.lth.se:8081/parse", data=post_data)
returncode = url.getcode()
content = url.read()
#if returncode != 200:
# print >> log,'NLP server error (problem processing)'
#print content
#print type(content)
content = content.split('\n')
sent = ""
#result={}
#SemPar = 0
#NoSbj = 0
#print "data is", repr(content)
#print content
for row in content:
table = row.split('\t')
#sent.append([table[0], table[1], table[2], table[4], table[5], table[6], table[8], table[10], table[12], table[13], "\n"])
sent += table[0] + "\t" + table[1] + "\t" + table[2] + "\t" + table[
4] + "\t" + table[5] + "\t" + table[6] + "\t" + table[
8] + "\t" + table[10] + "\t" + table[12] + "\t" + table[
13] + "\n"
#sent+=table[0]+"\t"+table[1]+"\n"
print sent
dg = DependencyGraph(sent)
tree = dg.tree()
print tree.pprint()
#print(dg)
print(dg.to_conll(4))
def test(parser, corpus, device, prt=False, gap=0):
"""Compute UF1 and UAS scores.
Args:
parser: pretrained model
corpus: labeled corpus
device: cpu or gpu
prt: bool, whether print examples
gap: distance gap for building non-binary tree
Returns:
UF1: unlabeled F1 score for constituency parsing
"""
parser.eval()
prec_list = []
reca_list = []
f1_list = []
dtree_list = []
corpus_sys = {}
corpus_ref = {}
nsens = 0
word2idx = corpus.dictionary.word2idx
dataset = zip(corpus.test_sens, corpus.test_trees, corpus.test_nltktrees)
for sen, sen_tree, sen_nltktree in dataset:
x = [word2idx[w] if w in word2idx else word2idx['<unk>'] for w in sen]
data = torch.LongTensor([x]).to(device)
pos = torch.LongTensor([list(range(len(sen)))]).to(device)
_, p_dict = parser(data, pos)
block = p_dict['block']
cibling = p_dict['cibling']
head = p_dict['head']
distance = p_dict['distance']
height = p_dict['height']
distance = distance.clone().squeeze(0).cpu().numpy().tolist()
height = height.clone().squeeze(0).cpu().numpy().tolist()
head = head.clone().squeeze(0).cpu().numpy()
max_height = numpy.max(height)
parse_tree = tree_utils.build_tree(distance, sen, gap=gap)
model_out, _ = tree_utils.get_brackets(parse_tree)
std_out, _ = tree_utils.get_brackets(sen_tree)
overlap = model_out.intersection(std_out)
corpus_sys[nsens] = tree_utils.mrg(parse_tree)
corpus_ref[nsens] = tree_utils.mrg_labeled(sen_nltktree)
prec = float(len(overlap)) / (len(model_out) + 1e-8)
reca = float(len(overlap)) / (len(std_out) + 1e-8)
if not std_out:
reca = 1.
if not model_out:
prec = 1.
f1 = 2 * prec * reca / (prec + reca + 1e-8)
prec_list.append(prec)
reca_list.append(reca)
f1_list.append(f1)
new_words = []
true_words = sen_nltktree.pos()
for d, c, w, ph in zip(distance, height, sen, head):
next_word = true_words.pop(0)
while next_word[1] not in data_ptb.WORD_TAGS:
next_word = true_words.pop(0)
new_words.append({
'address':
len(new_words) + 1,
'word':
next_word[0],
'lemma':
None,
'ctag':
None,
'tag':
next_word[1],
'feats':
None,
'head':
numpy.argmax(ph) + 1 if c < max_height else 0,
'deps':
collections.defaultdict(list),
'rel':
None,
'distance':
d,
'height':
c
})
while true_words:
next_word = true_words.pop(0)
assert next_word[1] not in data_ptb.WORD_TAGS
dtree = DependencyGraph()
for w in new_words:
dtree.add_node(w)
dtree_list.append(dtree)
if prt and len(dtree_list) % 100 == 0:
cibling = cibling.clone().squeeze(0).cpu().numpy()
block = block.clone().squeeze(0).cpu().numpy()
for word_i, d_i, imp_i, block_i, cibling_i, head_i in zip(
sen, distance, height, block, cibling, head):
print('%20s\t%10.2f\t%5.2f\t%s\t%s\t%s' %
(word_i, d_i, imp_i, plot(block_i, max_val=1.),
plot(head_i, max_val=1), plot(cibling_i, max_val=1.)))
print('Standard output:', sen_tree)
print('Model output:', parse_tree)
print(dtree.to_conll(10))
print()
fig_i, ax_i = plt.subplots()
ax_i.set_xticks(numpy.arange(len(sen)))
ax_i.set_yticks(numpy.arange(len(sen)))
ax_i.set_xticklabels(sen)
ax_i.set_yticklabels(sen)
plt.setp(ax_i.get_xticklabels(),
rotation=45,
ha='right',
rotation_mode='anchor')
for row in range(len(sen)):
for col in range(len(sen)):
_ = ax_i.text(col,
row,
'%.2f' % (head[row, col]),
ha='center',
va='center',
color='w')
fig_i.tight_layout()
plt.savefig('./figures/sentence-%d.png' % (len(dtree_list)),
dpi=300,
format='png')
nsens += 1
print('Constituency parsing performance:')
print('Mean Prec: %.4f, Mean Reca: %.4f, Mean F1: %.4f' %
(mean(prec_list), mean(reca_list), mean(f1_list)))
correct, total = tree_utils.corpus_stats_labeled(corpus_sys, corpus_ref)
print(correct)
print(total)
print('SBAR: %.4f' % (correct['SBAR'] / total['SBAR']))
print('NP: %.4f' % (correct['NP'] / total['NP']))
print('VP: %.4f' % (correct['VP'] / total['VP']))
print('PP: %.4f' % (correct['PP'] / total['PP']))
print('ADJP: %.4f' % (correct['ADJP'] / total['ADJP']))
print('ADVP: %.4f' % (correct['ADVP'] / total['ADVP']))
print(tree_utils.corpus_average_depth(corpus_sys))
print('-' * 89)
print('Dependency parsing performance:')
print('Stanford Style:')
tree_utils.evald(dtree_list, '../data/ptb/test.stanford', directed=True)
tree_utils.evald(dtree_list, '../data/ptb/test.stanford', directed=False)
print('Conll Style:')
tree_utils.evald(dtree_list, '../data/ptb/test.conll', directed=True)
tree_utils.evald(dtree_list, '../data/ptb/test.conll', directed=False)
return mean(f1_list)
