def to_list_repr_both(self, entity, document):
covered_tokens = document.cover_index[entity.uid]
if len(covered_tokens) > self.maxlen:
covered_tokens = covered_tokens[:self.maxlen]
context_left = []
context_right = []
else:
cl_len = int(math.floor((self.maxlen - len(covered_tokens)) / 2))
cr_len = self.maxlen - len(covered_tokens) - cl_len
context_left = document.tokens[max(0, covered_tokens[0].tid -
cl_len):covered_tokens[0].tid]
context_right = document.tokens[covered_tokens[-1].tid + 1:min(
len(document.tokens), covered_tokens[-1].tid + 1 + cr_len)]
# pad
context_left = [reader.Token(-1, -1, "#BEGIN_OF_TEXT#")
] * (cl_len - len(context_left)) + context_left
context_right = context_right + [
reader.Token(-1, -1, "#END_OF_TEXT#")
] * (cr_len - len(context_right))
left = [t.string for t in context_left]
right = [t.string for t in context_right]
covered = [t.string for t in covered_tokens]
words = left + covered + right
words_indexes = []
for w in words:
if w.lower() in self.word_index:
words_indexes.append(self.word_index[w.lower()])
else:
words_indexes.append("-1")
if len(words_indexes) != self.maxlen:
raise ValueError("Only", len(words_indexes),
"words, but should be", self.maxlen)
return words_indexes
def segment_text(text):
sentence_id = 0
token_id = 0
tail = text
accumulator = 0
sentences = [sentence for sentence in SentenceSplitter().split(text)]
sentence_object_array = []
for sentence in sentences:
escaped_sentence = re.escape(sentence)
sentence_occurrence = re.search(escaped_sentence, tail)
s_start, s_end = sentence_occurrence.span()
sentence_start = accumulator + s_start
sentence_end = accumulator + s_end
tokens = [word for word in word_tokenize(sentence)]
token_object_array = []
tail_for_token_search = sentence
token_accumulator = 0
for token in tokens:
escaped_token = re.escape(token)
token_occurrence = re.search(escaped_token, tail_for_token_search)
t_start, t_end = token_occurrence.span()
# global offsets
token_start = sentence_start + token_accumulator + t_start
token_end = sentence_start + token_accumulator + t_end
token_accumulator += t_end
token_object = reader.Token(token_start, token_end, utf8ify(token),
token_id)
token_object_array.append(token_object)
# keep searching in the rest
tail_for_token_search = tail_for_token_search[t_end:]
token_id += 1
sentence_object = reader.Sentence(sentence_start,
sentence_end, token_object_array,
utf8ify(sentence), sentence_id)
sentence_object_array.append(sentence_object)
for tok in sentence_object.token_array:
tok.sentence = sentence_object
accumulator += s_end
# keep rest of text for searching
tail = tail[s_end:]
sentence_id += 1
return sentence_object_array
def to_list_repr(self, entity, document):
covered_tokens = document.cover_index[entity.uid]
words = document.tokens[covered_tokens[0].tid:][:self.maxlen]
words = words + [reader.Token(-1, -1, "#END_OF_TEXT#")
] * (self.maxlen - len(words))
words = [t.string for t in words]
words_indexes = []
for w in words:
if w.lower() in self.word_index:
words_indexes.append(self.word_index[w.lower()])
else:
words_indexes.append("-1")
if len(words_indexes) != self.maxlen:
raise ValueError("Only", len(words_indexes),
"words, but should be", self.maxlen)
return words_indexes
def to_repr(self, entity, document):
covered_tokens = document.cover_index[entity.uid]
domain = document.domain
if self.sentence_boundaries:
span = covered_tokens[0].sentence
first_token = (span.token_array[0]).tid
last_token = (span.token_array[-1]).tid
else:
span = reader.Token(document.tokens[0].start,
document.tokens[-1].end, "")
first_token = 0
last_token = len(document.tokens) - 1
left_min_index = max(first_token, covered_tokens[0].tid - self.window)
left_max_index = covered_tokens[0].tid
if left_max_index <= left_min_index:
context_left = []
else:
context_left = document.tokens[left_min_index:left_max_index]
right_min_index = covered_tokens[-1].tid + 1
right_max_index = min(last_token,
covered_tokens[-1].tid + self.window + 1)
if right_min_index >= right_max_index:
context_right = []
else:
context_right = document.tokens[right_min_index:right_max_index]
cl = len(context_left)
cr = len(context_right)
K = self.vsm.dim
context_left = [
reader.Token(span.start - 1, span.start - 1, "#BEGIN_OF_SENTENCE#")
] * (self.window - cl) + context_left
context_right = context_right + [
reader.Token(span.end + 1, span.end + 1, "#END_OF_SENTENCE#")
] * (self.window - cr)
# take average embedding as representation
covered_emb = np.mean(
[self.vsm.get(t.string, domain) for t in covered_tokens], axis=0)
# take concatenated embedding as representation
# keep only the first m tokens: improve upon this
m = 4
if len(covered_tokens) > m:
# # simple heuristic: kick out short words
for t in covered_tokens:
if len(t.string) <= 3:
covered_tokens.remove(t)
if len(covered_tokens) <= m:
break
# covered_tokens = filter(lambda x: len(t.string)>3,covered_tokens)
my_center = np.concatenate(
[self.vsm.get(t.string, domain) for t in covered_tokens])
covered_emb = sequence.pad_sequences([my_center],
m * K,
truncating="post",
dtype="float32")[0]
context_left_emb = np.concatenate(
[self.vsm.get(t.string, domain) for t in context_left])
context_right_emb = np.concatenate(
[self.vsm.get(t.string, domain) for t in context_right])
# check if it is alright
print([t.string for t in context_left],
[t.string for t in covered_tokens],
[t.string for t in context_right])
return np.concatenate(
(context_left_emb, covered_emb, context_right_emb), axis=0)
def to_repr(self, entity, document):
covered_tokens = document.cover_index[entity.uid]
if self.sentence_boundaries:
span = covered_tokens[0].sentence
first_token = (span.token_array[0]).tid
last_token = (span.token_array[-1]).tid
else:
span = reader.Token(document.tokens[0].start,
document.tokens[-1].end, "")
first_token = 0
last_token = len(document.tokens) - 1
left_min_index = max(first_token, covered_tokens[0].tid - self.window)
left_max_index = covered_tokens[0].tid
if left_max_index <= left_min_index:
context_left = []
else:
context_left = document.tokens[left_min_index:left_max_index]
right_min_index = covered_tokens[-1].tid + 1
right_max_index = min(last_token,
covered_tokens[-1].tid + self.window + 1)
if right_min_index >= right_max_index:
context_right = []
else:
context_right = document.tokens[right_min_index:right_max_index]
cl = len(context_left)
cr = len(context_right)
K = 100
context_left = [reader.Token(span.start - 1, span.start - 1, "")
] * (self.window - cl) + context_left
context_right = context_right + [
reader.Token(span.end + 1, span.end + 1, "")
] * (self.window - cr)
token_representation_covered = " ".join(
[t.string for t in covered_tokens])
token_representation_left = " ".join([t.string for t in context_left])
token_representation_right = " ".join(
[t.string for t in context_right])
my_repr = []
# this is a bit stupid
# for submission, let's just do the character2index mapping offline
for x in token_representation_covered:
if x not in self.globalHash:
self.globalHash[x] = self.curVal
self.curVal += 1
my_repr.append(self.globalHash[x])
my_repr_left = []
for x in token_representation_left:
if x not in self.globalHash:
self.globalHash[x] = self.curVal
self.curVal += 1
my_repr_left.append(self.globalHash[x])
my_repr_right = []
for x in token_representation_right:
if x not in self.globalHash:
self.globalHash[x] = self.curVal
self.curVal += 1
my_repr_right.append(self.globalHash[x])
#print("%s\t%s\t%s"%(token_representation_left, token_representation_covered, token_representation_right))
my_repr = list(sequence.pad_sequences([my_repr], self.M)[0])
my_repr_left = list(sequence.pad_sequences([my_repr_left], self.L)[0])
my_repr_right = list(
sequence.pad_sequences([my_repr_right], self.R)[0])
return my_repr_left + my_repr + my_repr_right
