Word2Vec教程： Skip-Gram模型解析

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"word2vec是自然语言处理领域中一种用于编码单词语义信息的神经网络模型。它通过无监督学习的方式，从大量未标注的文本数据中生成每个单词的向量表示，使得这些向量能够反映单词的语义含义。在word2vec中有两种主要模型：Continuous Bag of Words (CBOW) 和 Skip-Gram模型。本教程主要关注Skip-Gram模型。 Skip-Gram模型的基本思想是，给定一个中心词（context word），尝试预测其周围的上下文词（context words）。这与CBOW模型相反，CBOW是通过上下文词来预测中心词。Skip-Gram模型能够更好地捕捉到单词的分布假设，即相似的上下文往往出现在相似的单词周围。在Skip-Gram模型中，训练过程通常包括两个步骤：前向传播和负采样。在前向传播阶段，当前词的向量被输入到神经网络中，然后通过softmax函数计算出每个上下文词的概率。负采样是优化过程中的一部分，它避免了在大规模词汇表中计算所有词对概率的计算复杂性，通过随机选择一部分“负样本”进行对比学习，以提高训练效率。 word2vec模型生成的向量具有许多有趣的特性。例如，通过计算两个单词向量的余弦相似度，我们可以量化它们之间的语义相似度。这种相似度可以用于诸如文档分类、命名实体识别和情感分析等监督任务，因为这些向量包含了丰富的语义信息，可以作为有效的特征。为了验证word2vec向量是否有效地捕捉了单词的语义，研究者们进行了多个实验。其中最著名的就是“国王-男性+女性=王后”这样的词汇关系推理。如果向量空间中的这种线性关系能够成立，那么说明模型确实编码了单词之间的语义联系。此外，word2vec向量还在词义消歧、翻译任务和推荐系统等领域展现出强大的应用潜力。 word2vec通过Skip-Gram模型提供了单词的分布式表示，这些表示不仅包含了单词的统计信息，还捕获了语义和句法结构。这一技术对于自然语言处理的诸多任务而言，是一个极其有价值的工具，极大地推动了NLP领域的发展。"

byAlex Minnaar onSun 12 April 2015

Category: Deep Learning

Latent Dirichlet Allocation in

Scala Part II - The Code

Word2Vec Tutorial Part I: The Skip-

Gram Model

In many natural language processing tasks, words are often represented by their tf-

idf scores. While these scores give us some idea of a word's relative importance in a

document, they do not give us any insight into its semantic meaning. Word2Vec is

the name given to a class of neural network models that, given an unlabelled

training corpus, produce a vector for each word in the corpus that encodes its

semantic information. These vectors are usefull for two main reasons.

1. We can measure the semantic similarity between two words are by calculating the

cosine similarity between their corresponding word vectors.

2. We can use these word vectors as features for various supervised NLP tasks such as

document classification, named entity recognition, and sentiment analysis. The

semantic information that is contained in these vectors make them powerful features

for these tasks.

You may ask "how do we know that these vectors effectively capture the semantic

meanings of the words?". The answer is because the vectors adhere surprisingly well

to our intuition. For instance, words that we know to be synonyms tend to have

similar vectors in terms of cosine similarity and antonyms tend to have dissimilar

vectors. Even more surprisingly, word vectors tend to obey the laws of analogy. For

example, consider the analogy "Woman is to queen as man is to king". It turns out

that

where , , , and are the word vectors for , ,

, and respectively. These observations strongly suggest that word vectors

encode valuable semantic information about the words that they represent.

In this series of blog posts I will describe the two main Word2Vec models - the

skip-gram model and the continuous bag-of-words model.

Both of these models are simple neural networks with one hidden layer. The word

− + ≈

queen

woman

man

king

queen

woman

man

king

queen

woman

man

king

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