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 __evaluate_edl(gold_edl_file, sys_edl_file, require_type_match, link_error_file,
type_error_file):
gold_mentions = Mention.load_edl_file(gold_edl_file, arrange_by_docid=True)
sys_mentions = Mention.load_edl_file(sys_edl_file, arrange_by_docid=True)
link_errors = list()
type_errors = list()
sys_cnt, gold_cnt, hit_cnt = 0, 0, 0
for docid, sys_mentions_doc in sys_mentions.iteritems():
gold_mentions_doc = gold_mentions.get(docid, list())
for gm in gold_mentions_doc:
if not gm.kbid.startswith('NIL'):
gold_cnt += 1
hit_list = [False for _ in xrange(len(gold_mentions_doc))]
for sm in sys_mentions_doc:
for i, gm in enumerate(gold_mentions_doc):
if sm.beg_pos == gm.beg_pos and sm.end_pos == gm.end_pos:
hit_list[i] = True
break
if sm.kbid.startswith('NIL'):
continue
sys_cnt += 1
for i, gm in enumerate(gold_mentions_doc):
if sm.beg_pos != gm.beg_pos or sm.end_pos != gm.end_pos:
continue
if gm.mention_type == 'NOM':
sys_cnt -= 1
break
if sm.entity_type != gm.entity_type:
type_errors.append((gm, sm))
if sm.kbid == gm.kbid and ((not require_type_match) or sm.entity_type == gm.entity_type):
hit_cnt += 1
if sm.kbid != gm.kbid:
link_errors.append((gm, sm))
# print '%s\t%s\t%s\t%s' % (docid, sm.mid, gm.mid, gm.name)
# print sm.mid, gm.mid, gm.name, docid
link_errors.sort(key=lambda x: x[0].name)
__write_link_errors(link_errors, link_error_file)
type_errors.sort(key=lambda x: x[0].name)
__write_type_errors(type_errors, type_error_file)
# for v in errors:
# print '%s\t%s\t%s\t%s' % (v[0], v[1], v[2], v[3])
print '#hit: %d, #sys: %d, #gold: %d' % (hit_cnt, sys_cnt, gold_cnt)
hit_cnt = float(hit_cnt)
prec = hit_cnt / sys_cnt
recall = hit_cnt / gold_cnt
f1 = 2 * prec * recall / (prec + recall)
print 'prec: %f, recall: %f, f1: %f' % (prec, recall, f1)
def __find_type_errors(gold_edl_file, sys_edl_file):
gold_mentions_docs = Mention.load_edl_file(gold_edl_file, arrange_by_docid=True)
sys_mentions_docs = Mention.load_edl_file(sys_edl_file, arrange_by_docid=True)
all_errors = list()
for docid, sys_mentions in sys_mentions_docs.iteritems():
gold_mentions = gold_mentions_docs[docid]
all_errors += __find_type_errors_of_docs(docid, gold_mentions, sys_mentions)
all_errors.sort(key=lambda x: x[0].name.lower())
for v in all_errors:
print '%s\t%s\t%s\t%s' % (v[0].name, v[0].entity_type, v[1].entity_type, v[0].docid)
def __nil_clustering(nom_dict_file, edl_file, dst_file):
nom_names = load_nom_dict(nom_dict_file)
all_mentions = Mention.load_edl_file(edl_file)
nil_mentions = [m for m in all_mentions if m.kbid.startswith('NIL') and m.name.lower() not in nom_names]
kbid_mentions = __group_mentions_by_kbid(nil_mentions)
new_kbids, new_mentions_kbids = list(), list()
for kbid, mentions in kbid_mentions.iteritems():
merged = False
for nkbid, nmentions in izip(new_kbids, new_mentions_kbids):
if __should_merge(mentions, nmentions):
# for m in mentions:
# print '%s\t' % m.name,
# print
# for m in nmentions:
# print '%s\t' % m.name,
# print '\n'
for m in mentions:
m.kbid = nkbid
nmentions.append(m)
merged = True
break
if not merged:
new_kbids.append(kbid)
new_mentions_kbids.append(mentions)
Mention.save_as_edl_file(all_mentions, dst_file)
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 __gold_mention_insight():
edl_gold_file = 'e:/el/LDC2015E103/data/tac_kbp_2015_tedl_evaluation_gold_standard_entity_mentions.tab'
mentions = Mention.load_edl_file(edl_gold_file)
doc_mention_dict = dict()
for m in mentions:
if m.docid.startswith('ENG'):
mlist = doc_mention_dict.get(m.docid, list())
if not mlist:
doc_mention_dict[m.docid] = mlist
mlist.append(m)
cnt, fncnt = 0, 0
for docid, doc_mentions in doc_mention_dict.iteritems():
print docid
for m0 in doc_mentions:
if m0.entity_type == 'PER' and ' ' in m0.name:
fncnt += 1
for m1 in doc_mentions:
if m0 == m1:
continue
if m0.beg_pos <= m1.beg_pos and m0.end_pos >= m1.end_pos and m0.entity_type == 'PER':
print '\t%s\t%d\t%d' % (m0.name, m0.beg_pos, m0.end_pos)
print '\t%s\t%d\t%d' % (m1.name, m1.beg_pos, m1.end_pos)
cnt += 1
# print m0.name, m0.beg_pos, m0.end_pos
# print m1.name, m1.beg_pos, m1.end_pos
print cnt, fncnt
def __check_mention_fb_types():
tac_edl_file = 'e:/el/LDC2015E75/data/tac_kbp_2015_tedl_training_gold_fixed.tab'
fb_type_file = 'e:/el/res/freebase/mid-fb-type.gz'
result_file = 'e:/el/LDC2015E75/data/mention-fb-types.txt'
mentions = Mention.load_edl_file(tac_edl_file)
mid_mentions = dict()
for m in mentions:
if m.mid.startswith('NIL'):
continue
mid_mentions[m.mid[2:]] = m
f = gzip.open(fb_type_file, 'r')
fout = open(result_file, 'wb')
for i, line in enumerate(f):
vals = line[:-1].split('\t')
m = mid_mentions.get(vals[0], None)
if m:
# print '%s\t%s\t%s\t%s' % (m.name, vals[0], m.entity_type, vals[1])
fout.write('%s\t%s\t%s\t%s\n' % (m.name.encode('utf-8'), vals[0], m.entity_type, vals[1]))
if (i + 1) % 1000000 == 0:
print i + 1
f.close()
fout.close()
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 __type_eval():
tac_edl_file = 'e:/el/LDC2015E75/data/tac_kbp_2015_tedl_training_gold_fixed.tab'
mid_type_file = 'e:/el/res/freebase/mid-entity-type.txt'
mid_type_dict = dict()
f = open(mid_type_file, 'r')
for line in f:
vals = line[:-1].split('\t')
mid_type_dict[vals[0]] = vals[1]
f.close()
hitcnt, cnt = 0, 0
mentions = Mention.load_edl_file(tac_edl_file)
for m in mentions:
if not m.mid.startswith('m.'):
continue
# print m.mid
cnt += 1
sys_type = mid_type_dict.get(m.mid[2:], 'ORG')
if sys_type == m.entity_type:
hitcnt += 1
else:
print m.mid, m.entity_type, sys_type
print hitcnt, cnt
print float(hitcnt) / cnt
def __gen_ttl_dict():
edl_file = '/home/dhl/data/EDL/LDC2015E103/data/gold-eng-mentions.tab'
dst_file = '/home/dhl/data/EDL/LDC2015E75/data/ttl-dict.txt'
mentions = Mention.load_edl_file(edl_file)
for m in mentions:
if m.entity_type == 'TTL':
print m.name, m.entity_type, m.mention_type, m.docid
def main():
# dataset = 'LDC2015E75'
dataset = 'LDC2015E103'
# dataset = 'LDC2016E63'
mentions_tag = '0'
run_id = 4
# datadir = '/home/dhl/data/EDL/'
datadir = 'e:/data/edl'
doc_list_file = os.path.join(datadir, dataset, 'data/eng-docs-list-win.txt')
mid_type_file = os.path.join(datadir, 'res/freebase/mid-entity-type.txt')
cur_edl_file = os.path.join(datadir, dataset, 'output/sys-link-sm-%s.tab' % mentions_tag)
miss_match_mentions_file = os.path.join(datadir, dataset, 'output/miss-match-mentions-%s.txt' % mentions_tag)
new_edl_file = os.path.join(datadir, dataset, 'output/sys-link-sm-pp-ft-%d.tab' % run_id)
# __nil_clustering(nom_dict_file, edl_file, dst_file)
mentions = Mention.load_edl_file(cur_edl_file)
# __link_nom(doc_mentions_dict, max_nil_id)
__nil_author_clustering(mentions)
__fix_special_types(mentions)
__fix_entity_types_by_mid(mid_type_file, mentions)
# __fix_type_diff_of_same_kbid(mentions)
__validate_mentions(doc_list_file, mentions, miss_match_mentions_file)
__fix_pos_error(mentions)
Mention.save_as_edl_file(mentions, new_edl_file, runid='WednesdayGo%d' % run_id)
def __evaluate_ed(gold_edl_file, sys_edl_file, fn_file, fp_file, require_type_match=True):
gold_mentions = Mention.load_edl_file(gold_edl_file, arrange_by_docid=True)
sys_mentions = Mention.load_edl_file(sys_edl_file, arrange_by_docid=True)
fout_fp = open(fp_file, 'wb')
sys_cnt, gold_cnt, hit_cnt = 0, 0, 0
fn_mentions = list()
for docid, sys_mentions_doc in sys_mentions.iteritems():
sys_cnt += len(sys_mentions_doc)
all_gold_mentions_in_doc = gold_mentions.get(docid, list())
# nam_gold_mentions = [m for m in all_gold_mentions_in_doc if m.mention_type == 'NAM']
nam_gold_mentions = all_gold_mentions_in_doc
gold_hit_tags = [False] * len(nam_gold_mentions)
gold_cnt += len(nam_gold_mentions)
for sm in sys_mentions_doc:
hit = False
for i, gm in enumerate(nam_gold_mentions):
type_hit = (sm.entity_type.startswith(gm.entity_type)) if require_type_match else True
if sm.beg_pos == gm.beg_pos and sm.end_pos == gm.end_pos and type_hit:
hit = True
hit_cnt += 1
gold_hit_tags[i] = True
break
if not hit:
fout_fp.write('%s\t%s\t%d\t%d\n' % (sm.name.encode('utf-8'), docid, sm.beg_pos, sm.end_pos))
# break
for gm, hit in izip(nam_gold_mentions, gold_hit_tags):
if not hit:
fn_mentions.append(gm)
# fout_fn.write('%s\t%s\t%d\t%d\n' % (gm.name.encode('utf-8'), docid, gm.beg_pos, gm.end_pos))
fout_fp.close()
fn_mentions.sort(key=lambda x: x.name)
Mention.write_mentions(fn_mentions, fn_file)
print '#hit: %d, #sys: %d, #gold: %d' % (hit_cnt, sys_cnt, gold_cnt)
hit_cnt = float(hit_cnt)
prec = hit_cnt / sys_cnt
recall = hit_cnt / gold_cnt
f1 = 2 * prec * recall / (prec + recall)
print 'prec: %f, recall: %f, f1: %f' % (prec, recall, f1)
def __remove_leading_the(metions_file, dst_mentions_edl_file):
mentions = Mention.load_edl_file(metions_file)
for m in mentions:
if m.name.startswith('the '):
m.name = m.name[4:]
m.beg_pos += 4
Mention.save_as_edl_file(mentions, dst_mentions_edl_file)
def __compare_mentions():
datadir = 'e:/data/edl'
edl_file0 = '%s/LDC2016E63/output/ner-mentions-0.tab' % datadir
edl_file1 = '%s/LDC2016E63/output/ner-mentions-1.tab' % datadir
mentions0 = Mention.load_edl_file(edl_file0, True)
mentions1 = Mention.load_edl_file(edl_file1, True)
for docid, doc_mentions1 in mentions1.iteritems():
print docid
doc_mentions0 = mentions0.get(docid, list())
for m1 in doc_mentions1:
found = False
for m0 in doc_mentions0:
if m0.beg_pos == m1.beg_pos and m0.end_pos == m1.end_pos:
found = True
break
if not found:
print '\t%s\t%d\t%d' % (m1.name, m1.beg_pos, m1.end_pos)
def __missing_docs_in_edl_file():
datadir = 'e:/data/edl'
edl_file = '%s/LDC2016E63/output/all-mentions.tab' % datadir
doc_list_file = '%s/LDC2016E63/data/eng-docs-list-win.txt' % datadir
mentions = Mention.load_edl_file(edl_file)
docids = set()
for m in mentions:
docids.add(m.docid)
f = open(doc_list_file, 'r')
for line in f:
doc_path = line.rstrip()
docid = doc_id_from_path(doc_path)
if docid not in docids:
print docid
f.close()
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 __gen_training_data(edl_file):
mentions = Mention.load_edl_file(edl_file)
nil_mentions = __get_nil_mentions(mentions)
kbid_mentions = Mention.group_mentions_by_kbid(nil_mentions)
pos_samples = __gen_positive_samples(kbid_mentions)
neg_samples = __gen_neg_samples(kbid_mentions, len(pos_samples))
data_x = list()
data_y = list()
all_samples = __merge_samples(pos_samples, neg_samples)
for sample, y, in all_samples:
sample_x = __get_features(sample)
data_x.append(sample_x)
data_y.append(y)
# print
for x, y in izip(data_x, data_y):
print x, y
return data_x, data_y
def __get_mid_types_in_dataset():
datadir = 'e:/data/edl'
edl_file = os.path.join(datadir, 'LDC2015E75/data/gold-eng-mentions.tab')
mid_types_file = os.path.join(datadir, 'res/freebase/mid-fb-type.gz')
dst_file = os.path.join(datadir, 'LDC2015E75/output/fb-types.txt')
mentions = Mention.load_edl_file(edl_file)
for m in mentions:
if m.kbid.startswith('m.'):
m.kbid = m.kbid[2:]
kbid_mentions = Mention.group_mentions_by_kbid(mentions)
f = gzip.open(mid_types_file, 'r')
fout = open(dst_file, 'wb')
hit = False
prev_kbid = ''
for i, line in enumerate(f):
tab_pos = line.find('\t')
kbid = line[:tab_pos]
if hit and prev_kbid == kbid:
fout.write('\t%s' % line)
elif prev_kbid != kbid:
if kbid in kbid_mentions:
cur_mentions = kbid_mentions[kbid]
for m in cur_mentions:
fout.write('%s\t' % m.name.encode('utf-8'))
fout.write('\n')
for m in cur_mentions:
fout.write('%s\t' % m.entity_type)
fout.write('\n\t%s' % line)
hit = True
else:
hit = False
prev_kbid = kbid
if (i + 1) % 10000000 == 0:
print i + 1
f.close()
fout.close()
def __merge_mentions(mention_file_list, dst_result_file):
mention_spans_docs = dict()
fout = open(dst_result_file, 'wb')
mention_id = 1
for mention_file in mention_file_list:
mentions = Mention.load_edl_file(mention_file)
for m in mentions:
mention_span = (m.beg_pos, m.end_pos)
mention_spans = mention_spans_docs.get(m.docid, set())
if not mention_spans:
mention_spans_docs[m.docid] = mention_spans
if mention_span in mention_spans:
continue
mention_spans.add(mention_span)
m.mention_id = 'EDL_%07d' % mention_id
# if m.entity_type.startswith('PER'):
# m.entity_type = 'PER'
m.to_edl_file(fout)
mention_id += 1
fout.close()
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)
def all_to_all(edl_file, dst_edl_file):
mentions = Mention.load_edl_file(edl_file)
__assgin_different_id_to_all_nils(mentions)
Mention.save_as_edl_file(mentions, dst_edl_file)
def __name_expansion(edl_mentions_file, doc_ner_file, tokenized_text_file,
entity_candidates_dict_file, dst_file):
mentions = Mention.load_edl_file(edl_mentions_file)
__expand_name_with_ner_result(mentions, doc_ner_file)
# __expand_location_names(mentions, tokenized_text_file, entity_candidates_dict_file)
Mention.save_as_edl_file(mentions, dst_file)
