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import argparse
parser = argparse.ArgumentParser(description='run matching using sentence embeddings and cosine similarity')
parser.add_argument('source_lang', help='source language short name')
parser.add_argument('target_lang', help='target language short name')
parser.add_argument('source_vector', help='path of the source vector')
parser.add_argument('target_vector', help='path of the target vector')
parser.add_argument('source_defs', help='path of the source definitions')
parser.add_argument('target_defs', help='path of the target definitions')
parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=2000, type=int)
args = parser.parse_args()
source_lang = args.source_lang
target_lang = args.target_lang
def load_embeddings(path, dimension = 300):
"""
Loads the embeddings from a word2vec formatted file.
The first line may or may not include the word count and dimension
"""
vectors = {}
with open(path, mode='r', encoding='utf8') as fp:
first_line = fp.readline().rstrip('\n')
if first_line.count(' ') == 1: # includes the "word_count dimension" information
(word_count, dimension) = map(int, first_line.split())
else: # assume the file only contains vectors
fp.seek(0)
for line in fp:
elems = line.split()
vectors[" ".join(elems[:-dimension])] = " ".join(elems[-dimension:])
return vectors
source_vectors_filename = args.source_vector
target_vectors_filename = args.target_vector
vectors_source = load_embeddings(source_vectors_filename)
vectors_target = load_embeddings(target_vectors_filename)
source_defs_filename = args.source_defs
target_defs_filename = args.target_defs
defs_source = [line.rstrip('\n') for line in open(source_defs_filename, encoding='utf8')]
defs_target = [line.rstrip('\n') for line in open(target_defs_filename, encoding='utf8')]
import numpy as np
from mosestokenizer import *
def clean_corpus_using_embeddings_vocabulary(
embeddings_dictionary,
corpus,
vectors,
language,
):
'''
Cleans corpus using the dictionary of embeddings.
Any word without an associated embedding in the dictionary is ignored.
'''
clean_corpus, clean_vectors, keys = [], {}, []
words_we_want = set(embeddings_dictionary)
tokenize = MosesTokenizer(language)
for key, doc in enumerate(corpus):
clean_doc = []
words = tokenize(doc)
for word in words:
if word in words_we_want:
clean_doc.append(word)
clean_vectors[word] = np.array(vectors[word].split()).astype(np.float)
if len(clean_doc) > 3 and len(clean_doc) < 25:
keys.append(key)
clean_corpus.append(' '.join(clean_doc))
tokenize.close()
return np.array(clean_corpus), clean_vectors, keys
clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary(
set(vectors_source.keys()),
defs_source,
vectors_source,
source_lang,
)
clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
set(vectors_target.keys()),
defs_target,
vectors_target,
target_lang,
)
import random
take = args.instances
common_keys = set(src_keys).intersection(set(target_keys))
take = min(len(common_keys), take) # you can't sample more than length
experiment_keys = random.sample(common_keys, take)
instances = len(experiment_keys)
clean_src_corpus = list(clean_src_corpus[experiment_keys])
clean_target_corpus = list(clean_target_corpus[experiment_keys])
print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}')
del vectors_source, vectors_target, defs_source, defs_target
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
vocab_counter = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
common = [w for w in vocab_counter.get_feature_names() if w in clean_src_vectors or w in clean_target_vectors]
W_common = []
for w in common:
if w in clean_src_vectors:
W_common.append(np.array(clean_src_vectors[w]))
else:
W_common.append(np.array(clean_target_vectors[w]))
print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}')
from sklearn.preprocessing import normalize
W_common = np.array(W_common)
W_common = normalize(W_common) # default is l2
vect_tfidf = TfidfVectorizer(vocabulary=common, dtype=np.double, norm='l2')
vect_tfidf.fit(clean_src_corpus + clean_target_corpus)
X_idf_source = vect_tfidf.transform(clean_src_corpus)
X_idf_target = vect_tfidf.transform(clean_target_corpus)
print(f'Matrices are {X_idf_source.shape} and {W_common.shape}')
print(f'The dimensions are {X_idf_source.ndim} and {W_common.ndim}')
X_idf_source_array = X_idf_source.toarray()
X_idf_target_array = X_idf_target.toarray()
S_emb_source = np.matmul(X_idf_source_array, W_common)
S_emb_target = np.matmul(X_idf_target_array, W_common)
S_emb_target_transpose = np.transpose(S_emb_target)
cost_matrix = np.matmul(S_emb_source, S_emb_target_transpose)
from lapjv import lapjv
cost_matrix = cost_matrix * -1000
row_ind, col_ind, a = lapjv(cost_matrix, verbose=False)
result = zip(row_ind, col_ind)
hit_one = len([x for x,y in result if x == y])
print(f'{hit_one} definitions have been mapped correctly, shape of cost matrix: {str(cost_matrix.shape)}')
import csv
percentage = hit_one / instances * 100
fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
with open('semb_matcing.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
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