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match_entities_by_distance.py
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#!/usr/bin/env python
from __future__ import print_function
from __future__ import print_function
import sys
from collections import defaultdict
from KafNafParserPy import KafNafParser
class Centity:
def __init__(self, line=None):
self.id = ''
self.type = ''
self.filename = ''
self.word_list = []
self.token_id_list = []
self.filename = ''
if line is not None:
self.load_from_line(line)
def create(self,this_id, this_type, this_filename, id_list, word_list):
self.id = this_id
self.type = this_type
self.filename = this_filename
self.word_list = word_list[:]
self.token_id_list = id_list[:]
def load_from_line(self,line):
fields = line.strip().split('\t')
self.type = fields[0]
self.word_list = fields[1].split(' ')
ids_with_filename = fields[2].split(' ')
for id_with_filename in ids_with_filename:
p = id_with_filename.rfind('#')
self.filename = id_with_filename[:p]
#self.token_id_list.append(id_with_filename)
self.token_id_list.append(id_with_filename[p+1:])
def to_line(self):
#in the DS we need to include also the filename
tokens_with_filename = [self.filename+'#'+token_id for token_id in self.token_id_list]
line = '%s\t%s\t%s' % (self.type,' '.join(self.word_list),' '.join(tokens_with_filename))
return line
def __str__(self):
s = ''
s += 'Type: %s\n' % self.type
s += 'Words: %s\n' % str(self.word_list)
s += 'Filename: %s\n' % self.filename
s += 'Ids: %s\n' % str(self.token_id_list)
return s
def get_avg_position(self, naf_obj):
offset_total = 0
for token_id in self.token_id_list:
token_obj = naf_obj.get_token(token_id)
offset_total += int(token_obj.get_offset())
avg_position = 1.0*offset_total/len(self.token_id_list)
return avg_position
def get_avg_position_num_tokens(self, naf_obj):
list_ids_offset = []
for token in naf_obj.get_tokens():
list_ids_offset.append((token.get_id(),int(token.get_offset())))
if hasattr(naf_obj, 'position_for_token'):
pass
else:
naf_obj.position_for_token = {}
numT = 0
for token_id, token_offset in sorted(list_ids_offset, key=lambda t: -t[1]):
naf_obj.position_for_token[token_id] = numT
numT += 1
position_total = 0
for token_id in self.token_id_list:
position = naf_obj.position_for_token[token_id]
position_total += position
avg_position = 1.0*position_total/len(self.token_id_list)
return avg_position
def get_sentence(self, naf_obj):
first_token = self.token_id_list[0]
token_obj = naf_obj.get_token(first_token)
sentence = token_obj.get_sent()
return sentence
def load_entities(filename):
list_entities = []
fd = open(filename,'r')
for line in fd:
entity = Centity(line)
list_entities.append(entity)
fd.close()
return list_entities
def match_entities(expression_entities, target_entities, knaf_obj):
matched_pairs = []
if len(expression_entities) > 0:
for target in target_entities:
target_sentence = target.get_sentence(knaf_obj)
#position_for_target = target.get_avg_position(knaf_obj)
position_for_target = target.get_avg_position_num_tokens(knaf_obj)
expressions_with_distance = []
#print 'Entity: ',expression.word_list, position_for_expression
for expression in expression_entities:
expression_sentence = expression.get_sentence(knaf_obj)
if target_sentence == expression_sentence:
#position_for_expression = expression.get_avg_position(knaf_obj)
position_for_expression = expression.get_avg_position_num_tokens(knaf_obj)
distance = abs(position_for_expression-position_for_target)
expressions_with_distance.append((expression,distance))
if len(expressions_with_distance) != 0:
expressions_with_distance.sort(key=lambda t: t[1])
#for target, d in expressions_with_distance:
# print '\t', target.word_list, d
#We select the first one
selected_expression = expressions_with_distance[0][0]
#print 'FIXED:', target
#for a,b in expressions_with_distance:
# print 'CANDIDATE', a.to_line(), b
#print
matched_pairs.append((selected_expression, target))
return matched_pairs
if __name__ == '__main__':
expression_filename = sys.argv[1] #test.mpqa.exp.csv
target_filename = sys.argv[2] #test.mpqa.tar.csv
expression_entities = load_entities(expression_filename)
target_entities = load_entities(target_filename)
target_entities_per_filename = defaultdict(list)
for t in target_entities:
target_entities_per_filename[t.filename].append(t)
for filename, list_targets in list(target_entities_per_filename.items()):
knaf_obj = KafNafParser(filename)
expression_candidates = []
for expression in expression_entities:
if expression.filename == filename:
expression_candidates.append(expression)
matched_pairs = match_entities(expression_candidates, list_targets, knaf_obj)
for exp, tar in matched_pairs:
print(exp.to_line())
print(tar.to_line())
print()