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from string import punctuation
import numpy as np
import torch
from sklearn.cluster import KMeans
from model.named_entities import get_named_entities
punctuation = [c for c in punctuation if c != "_"]
punctuation += ["“", "–", ",", "…", "”", "–"]
ethnicity_dict_map = {"H'Mông": "HMông",
"H'mông": "HMông",
"H’mông": "HMông",
"H’Mông": "HMông",
"H’MÔNG": "HMông",
"M'Nông": "MNông",
"M'nông": "MNông",
"M'NÔNG": "MNông",
"M’Nông": "MNông",
"M’NÔNG": "MNông",
"K’Ho": "KHo",
"K’Mẻo": "KMẻo"}
def sub_sentence(sentence):
sent = []
start_index = 0
while start_index < len(sentence):
idx_list = []
for p in punctuation:
idx = sentence.find(p, start_index)
if idx != -1:
idx_list.append(idx)
if len(idx_list) == 0:
sent.append(sentence[start_index:].strip())
break
end_index = min(idx_list)
subsent = sentence[start_index:end_index].strip()
if len(subsent) > 0:
sent.append(subsent)
start_index = end_index + 1
return sent
def check_for_stopwords(ngram, stopwords_ls):
for ngram_elem in ngram.split():
for w in stopwords_ls:
if ngram_elem == w: # or ngram_elem.lower() == w:
return True
return False
def compute_ngram_list(segmentised_doc, ngram_n, stopwords_ls, subsentences=True):
if subsentences:
output_sub_sentences = []
for sentence in segmentised_doc:
output_sub_sentences += sub_sentence(sentence)
else:
output_sub_sentences = segmentised_doc
ngram_list = []
for sentence in output_sub_sentences:
sent = sentence.split()
for i in range(len(sent) - ngram_n + 1):
ngram = ' '.join(sent[i:i + ngram_n])
if ngram not in ngram_list and not check_for_stopwords(ngram, stopwords_ls):
ngram_list.append(ngram)
final_ngram_list = []
for ngram in ngram_list:
contains_number = False
for char in ngram:
if char.isnumeric():
contains_number = True
break
if not contains_number:
final_ngram_list.append(ngram)
return final_ngram_list
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def get_doc_embeddings(segmentised_doc, tokenizer, phobert, stopwords):
doc_embedding = torch.zeros(size=(len(segmentised_doc), 768))
for i, sentence in enumerate(segmentised_doc):
sent_removed_stopwords = ' '.join([word for word in sentence.split() if word not in stopwords])
sentence_embedding = tokenizer.encode(sent_removed_stopwords)
input_ids = torch.tensor([sentence_embedding])
with torch.no_grad():
features = phobert(input_ids)
if i == 0:
doc_embedding[i, :] = 2 * features.pooler_output.flatten()
else:
doc_embedding[i, :] = features.pooler_output.flatten()
return torch.mean(doc_embedding, axis=0)
def get_segmentised_doc(nlp, rdrsegmenter, title, doc):
for i, j in ethnicity_dict_map.items():
if title is not None:
title = title.replace(i, j)
doc = doc.replace(i, j)
segmentised_doc = rdrsegmenter.word_segment(doc)
if title is not None:
segmentised_doc = rdrsegmenter.word_segment(title) + rdrsegmenter.word_segment(doc)
ne_ls = set(get_named_entities(nlp, doc))
segmentised_doc_ne = []
for sent in segmentised_doc:
for ne in ne_ls:
sent = sent.replace(ne, '_'.join(ne.split()))
segmentised_doc_ne.append(sent)
return ne_ls, segmentised_doc_ne
def compute_ngram_embeddings(tokenizer, phobert, ngram_list):
ngram_embeddings = {}
for ngram in ngram_list:
ngram_copy = ngram
if ngram.isupper():
ngram_copy = ngram.lower()
word_embedding = tokenizer.encode(ngram_copy)
input_ids = torch.tensor([word_embedding])
with torch.no_grad():
word_features = phobert(input_ids)
ngram_embeddings[ngram] = word_features.pooler_output
return ngram_embeddings
def compute_ngram_similarity(ngram_list, ngram_embeddings, doc_embedding):
ngram_similarity_dict = {}
for ngram in ngram_list:
similarity_score = cosine_similarity(ngram_embeddings[ngram], doc_embedding.T).flatten()[0]
# similarity_score = normalised_cosine_similarity(ngram_embeddings[ngram], doc_embedding.T).flatten()[0]
ngram_similarity_dict[ngram] = similarity_score
return ngram_similarity_dict
def diversify_result_kmeans(ngram_result, ngram_embeddings, top_n=5):
best_ngrams = sorted(ngram_result, key=ngram_result.get, reverse=True)[:top_n * 4]
best_ngram_embeddings = np.array([ngram_embeddings[ngram] for ngram in best_ngrams]).squeeze()
vote = {}
for niter in range(100):
kmeans = KMeans(n_clusters=top_n, init='k-means++', random_state=niter * 2, n_init="auto").fit(
best_ngram_embeddings)
kmeans_result = kmeans.labels_
res = {}
for i in range(len(kmeans_result)):
if kmeans_result[i] not in res:
res[kmeans_result[i]] = []
res[kmeans_result[i]].append((best_ngrams[i], ngram_result[best_ngrams[i]]))
final_result = [res[k][0] for k in res]
for keyword in final_result:
if keyword not in vote:
vote[keyword] = 0
vote[keyword] += 1
diversify_result_ls = sorted(vote, key=vote.get, reverse=True)
return diversify_result_ls[:top_n]
def remove_duplicates(ngram_result):
to_remove = set()
for ngram in ngram_result:
for ngram2 in ngram_result:
if ngram not in to_remove and ngram != ngram2 and ngram.lower() == ngram2.lower():
new_score = np.mean([ngram_result[ngram], ngram_result[ngram2]])
ngram_result[ngram] = new_score
to_remove.add(ngram2)
for ngram in to_remove:
ngram_result.pop(ngram)
return ngram_result
def compute_filtered_text(annotator, title, text):
annotated = annotator.annotate_text(text)
if title is not None:
annotated = annotator.annotate_text(title + '. ' + text)
filtered_sentences = []
keep_tags = ['N', 'Np', 'V', 'Nc']
for key in annotated.keys():
sent = ' '.join([dict_['wordForm'] for dict_ in annotated[key] if dict_['posTag'] in keep_tags])
filtered_sentences.append(sent)
return filtered_sentences
def get_candidate_ngrams(segmentised_doc, filtered_segmentised_doc, ngram_n, stopwords_ls):
# get actual ngrams
actual_ngram_list = compute_ngram_list(segmentised_doc, ngram_n, stopwords_ls, subsentences=True)
# get filtered ngrams
filtered_ngram_list = compute_ngram_list(filtered_segmentised_doc, ngram_n, stopwords_ls,
subsentences=False)
# get candidate ngrams
candidate_ngram = [ngram for ngram in filtered_ngram_list if ngram in actual_ngram_list]
return candidate_ngram
def remove_overlapping_ngrams(ngram_list):
to_remove = set()
for ngram1 in ngram_list:
for ngram2 in ngram_list:
if len(ngram1.split()) > len(ngram2.split()) and (ngram1.startswith(ngram2) or ngram1.endswith(ngram2)):
to_remove.add(ngram2)
for kw in to_remove:
ngram_list.remove(kw)
return ngram_list
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