Question

我尝试使用word2vec创建一个模型来确定另一个句子的最相似句子。

这个想法是确定一个句子中最相似的一个，我为组成这个句子的单词创建了一个平均向量。

然后，我应该使用嵌入词来预测最相似的句子。我的问题是：创建源句子的平均向量后，如何确定最佳的相似目标句子？

这里的代码：

import gensim
from gensim import utils
import numpy as np
import sys
from sklearn.datasets import fetch_20newsgroups
from nltk import word_tokenize
from nltk import download
from nltk.corpus import stopwords
import matplotlib.pyplot as plt

model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)


download('punkt') #tokenizer, run once
download('stopwords') #stopwords dictionary, run once
stop_words = stopwords.words('english')

def preprocess(text):
    text = text.lower()
    doc = word_tokenize(text)
    doc = [word for word in doc if word not in stop_words]
    doc = [word for word in doc if word.isalpha()] #restricts string to alphabetic characters only
    return doc
############  doc content  -> num label     -> string label
#note to self: texts[XXXX] -> y[XXXX] = ZZZ -> ng20.target_names[ZZZ]

# Fetch ng20 dataset
ng20 = fetch_20newsgroups(subset='all',
                          remove=('headers', 'footers', 'quotes'))
# text and ground truth labels
texts, y = ng20.data, ng20.target

corpus = [preprocess(text) for text in texts]

def filter_docs(corpus, texts, labels, condition_on_doc):
    """
    Filter corpus, texts and labels given the function condition_on_doc which takes
    a doc.
    The document doc is kept if condition_on_doc(doc) is true.
    """
    number_of_docs = len(corpus)
    print(number_of_docs)

    if texts is not None:
        texts = [text for (text, doc) in zip(texts, corpus)
                 if condition_on_doc(doc)]

    labels = [i for (i, doc) in zip(labels, corpus) if condition_on_doc(doc)]
    corpus = [doc for doc in corpus if condition_on_doc(doc)]

    print("{} docs removed".format(number_of_docs - len(corpus)))

    return (corpus, texts, labels)

corpus, texts, y = filter_docs(corpus, texts, y, lambda doc: (len(doc) != 0))

def document_vector(word2vec_model, doc):
    # remove out-of-vocabulary words
    #print("doc:")
    #print(doc)
    doc = [word for word in doc if word in word2vec_model.vocab]
    return np.mean(word2vec_model[doc], axis=0)

def has_vector_representation(word2vec_model, doc):
    """check if at least one word of the document is in the
    word2vec dictionary"""
    return not all(word not in word2vec_model.vocab for word in doc)

corpus, texts, y = filter_docs(corpus, texts, y, lambda doc: has_vector_representation(model, doc))

x =[]
for doc in corpus: #look up each doc in model

    x.append(document_vector(model, doc))


X = np.array(x) #list to array

model.most_similar(positive=X, topn=1)

Answer 1

仅使用余弦距离。在scipy中实现。

为了获得更高的效率，您可以自己实现并预先计算X中向量的范数：

X_norm = np.linalg.norm(X, axis=1).expand_dims(0)

调用expand_dims可确保广播尺寸。然后对于向量Y，您可以得到最相似的结果，您可以得到最相似的结果：

def get_most_similar_in_X(Y):
    Y_norm = np.linalg.norm(Y, axis=1).expand_dims(1)
    similarities = np.dot(Y, X.T) / Y_norm / X_norm
    return np.argmax(distances, axis=2)

您将在X中获得与Y中的向量最相似的向量的索引。

使用word2vec确定最相似的短语

1 个答案: