-
Notifications
You must be signed in to change notification settings - Fork 0
/
generator.py
202 lines (169 loc) · 7.15 KB
/
generator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
from nltk import sent_tokenize
import enum
from tagme import Annotation
import re
from text_processor import TextProcessor
from tagger import TagmeManager
class Platform(enum.Enum):
REDDIT = "reddit"
TWITTER = "twitter"
WIKI = "wiki"
def __str__(self):
return self.value
class GenerateCorpus:
def __init__(self, platform, platform_ext, platform_id, title, body):
self.platform = platform
self.platform_ext = platform_ext
self.platform_id = platform_id
self.title = title
self.body = body
self.text_processor = TextProcessor()
self.tagme_manager = TagmeManager(rho=0.15)
def adjust_entity_indices(self, original_text, entity):
cleaned_begin = entity['begin']
mention = entity['mention']
original_index = original_text.find(mention, cleaned_begin - 5 if cleaned_begin > 5 else 0)
if original_index != -1:
entity['begin'] = original_index
entity['end'] = original_index + len(mention)
else:
original_begin, original_end = self.find_closest_match(original_text, mention, cleaned_begin)
if original_begin is not None and original_end is not None:
entity['begin'] = original_begin
entity['end'] = original_end
return entity
@staticmethod
def find_closest_match(original_text, mention, start_index):
pattern = re.escape(mention)
for match in re.finditer(pattern, original_text, re.IGNORECASE):
if abs(match.start() - start_index) <= len(mention):
return match.start(), match.end()
return None, None
@staticmethod
def find_full_sentence(doc, start_idx, end_idx):
"""
This function finds the full sentence that contains the entity based on starting and ending index.
"""
sentences = sent_tokenize(doc.text)
for sentence in sentences:
if doc.text.find(sentence) <= start_idx and doc.text.find(sentence) + len(sentence) >= end_idx:
return sentence
return ""
def generate_corpus(self) -> dict:
corpus: dict = {
"platform": self.platform.value + "/" + self.platform_ext if self.platform_ext else self.platform.value,
"id": self.platform_id,
"title": self.title,
"body": self.body,
"version": 5}
# clean text - remove special characters, remove stopwords, lower case, etc
text_processor = TextProcessor()
# Replace \ character with space to prevent tagme api error
cleaned_body = text_processor.clean_text(self.body)
cleaned_title = text_processor.clean_text(self.title)
# NER (Named Entity Recognition) - tag entities in text
tagme_manager = TagmeManager(rho=0.15)
if cleaned_title == "" and cleaned_body == "":
return None
# ENTITIES #
if cleaned_title != "":
tagged_title: list[Annotation] = tagme_manager.tag_text(cleaned_title)
else:
tagged_title = []
if cleaned_body != "":
tagged_body: list[Annotation] = tagme_manager.tag_text(cleaned_body)
else:
tagged_body = []
# create entities with their base tagme information
entities: list[dict] = []
for annotation in tagged_title:
entity = {
"name": annotation.entity_title,
"location": "title",
"mention": annotation.mention,
"begin": annotation.begin,
"end": annotation.end,
"confidence": annotation.score,
"sentiment": None,
"wiki_id": annotation.entity_id,
"wiki_info": {},
}
info = tagme_manager.get_annotation_info(annotation)
entity['wiki_info'] = info
adjusted_entity = self.adjust_entity_indices(self.title, entity)
entities.append(adjusted_entity)
for annotation in tagged_body:
entity = {
"name": annotation.entity_title,
"location": "body",
"mention": annotation.mention,
"begin": annotation.begin,
"end": annotation.end,
"confidence": annotation.score,
"sentiment": None,
"wiki_id": annotation.entity_id,
"wiki_info": {},
}
info = tagme_manager.get_annotation_info(annotation)
entity['wiki_info'] = info
adjusted_entity = self.adjust_entity_indices(self.body, entity)
entities.append(adjusted_entity)
if len(entities) == 0:
return corpus
# filter entities that have their indices adjusted
for entity in entities:
if entity['location'] == 'title':
found_word = self.title[entity['begin']:entity['end']]
elif entity['location'] == 'body':
found_word = self.body[entity['begin']:entity['end']]
else:
found_word = None
if entity['mention'] != found_word:
# concurrent modification exception?
entities.remove(entity)
# Process title and body with NLP
title_doc = text_processor.nlp(self.title)
body_doc = text_processor.nlp(self.body)
for entity in entities:
if entity['location'] == 'title':
entity_doc = title_doc
elif entity['location'] == 'body':
entity_doc = body_doc
else:
continue # Skip if location is not title or body
# find the entity itself and its boundaries
dependent_tokens = []
for token in entity_doc:
if entity['begin'] <= token.idx < entity['end']:
dependent_tokens.append(token)
if not dependent_tokens:
continue
# Extend to full sentence for context
min_index = min(token.idx for token in dependent_tokens)
max_index = max(token.idx + len(token.text) for token in dependent_tokens)
full_sentence = self.find_full_sentence(entity_doc, min_index, max_index)
entity['sentence'] = full_sentence
comp, pos, neg, neu = text_processor.get_sentiment_scores(full_sentence)
entity['sentiment'] = {
"compound": comp,
"positive": pos,
"negative": neg,
"neutral": neu
}
corpus["entities"] = entities
# ENTITY_GROUPS #
entity_groups: list[object] = []
for entity in entities:
found = False
for group in entity_groups:
if group.get('sentence') == entity.get('sentence'):
group['entities'].append(entity['wiki_id'])
found = True
break
if not found:
entity_groups.append({
"sentence": entity.get('sentence', ''),
"entities": [entity['wiki_id']]
})
corpus["entity_groups"] = entity_groups
return corpus