def __list_errors():
gold_edl_file = 'e:/data/el/LDC2015E20/data/eval/data/mentions-raw.tab'
sys_edl_file = 'e:/data/el/LDC2015E20/data/eval/output/emadr-result-coref.tab'
eid_wid_file = 'e:/data/el/res/eid_wid_ord_eid.txt'
eid_wid_dict = load_eid_wid_file(eid_wid_file)
gold_mentions = Mention.load_edl_file(gold_edl_file)
gold_qid_mentions = Mention.group_mentions_by_qid(gold_mentions)
sys_mentions = Mention.load_edl_file(sys_edl_file)
sys_qid_mentions = Mention.group_mentions_by_qid(sys_mentions)
for qid, mention in gold_qid_mentions.iteritems():
sys_mention = sys_qid_mentions[qid]
if sys_mention.kbid == mention.kbid:
continue
if sys_mention.kbid.startswith('NIL') and mention.kbid.startswith(
'NIL'):
continue
if mention.kbid.startswith('NIL'):
continue
wid_gold = eid_wid_dict.get(mention.kbid, -1)
wid_sys = eid_wid_dict.get(sys_mention.kbid, -1)
print '%s\t%s\t%s\t%s\t%d\t%d\t%s' % (
qid, mention.kbid, sys_mention.kbid, mention.docid,
mention.beg_pos, mention.end_pos, mention.name)
print wid_gold, wid_sys
def __expand_location_names(mentions, tokenized_text_file,
entity_candidates_dict_file):
doc_mentions_dict = Mention.group_mentions_by_docid(mentions)
expansion_candidates = []
f = open(tokenized_text_file, 'r')
for line in f:
vals = line.strip().split('\t')
docid = vals[0]
# print docid
num_lines = int(vals[1])
doc_mentions = doc_mentions_dict[docid]
# print len(mentions)
for i in xrange(num_lines):
line = f.next().decode('utf-8')
words = line.strip().split(' ')
expansion_candidates += __find_expansion_candidates_in_location_mentions(
doc_mentions, words)
# break
# break
f.close()
expansion_dict = __filter_expansion_candidates(
expansion_candidates, entity_candidates_dict_file)
qid_mentions = Mention.group_mentions_by_qid(mentions)
for qid, mention in qid_mentions.iteritems():
exp_name = expansion_dict.get(qid, '')
if not exp_name:
continue
print '%s\t%s\t%s' % (qid, mention.name, exp_name)
mention.name = exp_name
def __apply_coref(edl_file, linking_info_file, dst_edl_file):
coref_dict = dict()
f = open(linking_info_file, 'rb')
while True:
docid = ioutils.read_str_with_byte_len(f)
if not docid:
break
num_mentions = np.fromfile(f, '>i4', 1)
is_nested = np.fromfile(f, 'b', num_mentions)
corefs = np.fromfile(f, '>i4', num_mentions)
qids = list()
for i in xrange(num_mentions):
qid = __read_mention_from_linking_info_file(f)
qids.append(qid)
for coref_id, qid in izip(corefs, qids):
if coref_id > 0:
coref_dict[qid] = qids[coref_id]
f.close()
mentions = Mention.load_edl_file(edl_file)
qid_mentions = Mention.group_mentions_by_qid(mentions)
__assgin_different_id_to_all_nils(mentions)
print qid_mentions['EDL14_ENG_0052'].kbid
for m in mentions:
if not m.kbid.startswith('NIL'):
continue
coref_qid = coref_dict.get(m.mention_id, '')
if coref_qid:
print m.mention_id, coref_qid, m.name, qid_mentions[coref_qid].kbid
m.kbid = qid_mentions[coref_qid].kbid
Mention.save_as_edl_file(mentions, dst_edl_file)
def __save_link_result(edl_file, result_triv, qids, kbids_list, y_pred,
max_scores, dst_file, use_nil_thres):
mentions = Mention.load_edl_file(edl_file)
for m in mentions:
m.kbid = 'NODEF'
qid_mentions = Mention.group_mentions_by_qid(mentions)
for qid, kbid in result_triv.iteritems():
qid_mentions[qid].kbid = kbid
# print qid, kbid
for qid, kbids, y, max_score in izip(qids, kbids_list, y_pred, max_scores):
if y >= len(kbids):
print y, len(kbids)
if qid_mentions[qid].kbid == 'NODEF':
if use_nil_thres and max_score < 0.5:
qid_mentions[qid].kbid = 'NIL'
else:
qid_mentions[qid].kbid = kbids[y]
# print qid, kbids[y]
for m in mentions:
if m.kbid.startswith('m.') or m.kbid.startswith('NIL'):
m.kbid = 'NIL0001'
Mention.save_as_edl_file(mentions, dst_file)
def __build_training_data(qid_x_list, edl_file):
mentions = Mention.load_edl_file(edl_file)
qid_mentions = Mention.group_mentions_by_qid(mentions)
train_x = list()
train_y = list()
for tup in qid_x_list:
qid, kbid, first_candidate, commonness, dist = tup
# print qid, kbid, first_candidate, commonness, dist
m = qid_mentions[qid]
if (not m.kbid.startswith('NIL')) and m.kbid != kbid:
continue
y = 0 if m.kbid.startswith('NIL') else 1
# train_x.append([first_candidate, commonness, dist])
train_x.append([first_candidate, commonness])
# train_x.append([first_candidate])
train_y.append(y)
return train_x, train_y
def __el_stat():
data_file = 'e:/data/emadr/el/tac/2009/eval/el-2009-eval-expansion-nloc-3.bin'
gold_file = 'e:/data/el/LDC2015E19/data/2009/eval/data/mentions-raw.tab'
# data_file = 'e:/data/emadr/el/tac/2011/eval/el-2011-eval-expansion-all-3.bin'
# gold_file = 'e:/data/el/LDC2015E19/data/2011/eval/data/mentions-expansion-all.tab'
# data_file = 'e:/data/emadr/el/tac/2014/eval/el-2014-eval-raw-%d.bin' % 3
# gold_file = 'e:/data/el/LDC2015E20/data/eval/data/mentions-raw.tab'
eid_wid_file = 'e:/data/el/res/eid_wid_ord_eid.txt'
keep_nil = True
only_show_not_in_candidate = False
eid_wid_dict = load_eid_wid_file(eid_wid_file)
# gold_el_result = load_gold_el(gold_file)
mentions = Mention.load_edl_file(gold_file)
qid_mention_dict = Mention.group_mentions_by_qid(mentions)
docs_info, dim = load_docs_info(data_file)
error_list = list()
num_mentions, nil_mentions = 0, 0
nil_hit_cnt, id_hit_cnt = 0, 0
for doc in docs_info:
docid, docvec, mentions = doc
for mention in mentions:
(qid, kbids, commonnesses, vecs) = mention
gold_mention = qid_mention_dict[qid]
gold_id = gold_mention.kbid
gold_id_is_nil = gold_id.startswith('NIL')
if gold_id_is_nil:
nil_mentions += 1
if not keep_nil and gold_id_is_nil:
continue
num_mentions += 1
indices, legal_kbids = __get_legal_kbids(kbids, keep_nil)
if gold_id_is_nil and (len(legal_kbids) == 0
or legal_kbids[0].startswith('m.')):
nil_hit_cnt += 1
continue
first_kbid = legal_kbids[0] if legal_kbids else 'NIL'
if first_kbid == gold_id:
id_hit_cnt += 1
continue
error_list.append(
(qid, docid, gold_mention.name, gold_id, legal_kbids))
error_list.sort(key=lambda x: x[2])
for e in error_list:
qid, docid, name, gold_id, legal_kbids = e
gold_wid = eid_wid_dict.get(gold_id, -1)
in_candidates = gold_id in legal_kbids
if only_show_not_in_candidate and in_candidates:
continue
# if not in_candidates:
# print 'not found'
print '%s\t%s\t%s\t%s_%d' % (qid, docid, name, gold_id, gold_wid)
# for eid in legal_kbids:
# wid = eid_wid_dict.get(eid, -1)
# print '\t%s_%d' % (eid, wid),
# print
print id_hit_cnt, num_mentions
print 'INKB: %f' % (float(id_hit_cnt) / (num_mentions - nil_mentions))
print 'TOTAL: %f' % (float(id_hit_cnt + nil_hit_cnt) / num_mentions)
