企业官网建设流程全解析

以下是我的学习笔记，以及总结，如有错误之处请不吝赐教。

离散表示：one-hot表示、bag of words（TF-IDF）、N-gram；

问题：无法衡量词向量之间的关系、词表维度随着语料库增长膨胀、数据稀疏问题、各种度量（距离、或与非）都不适合。

分布式表示(用一个词附近的其他词来表示该词）：word2vec、word-embedding。

skip-gram：

模型特性：无隐层、投隐层也可省略、每个词向量作为log-linear模型的输入

目标函数：

概率密度由softmax给出：

损失函数：

负例采样：P(w|context(w))： 一个正样本，V-1个负样本，对负样本做采样

pytorch实现核心代码：

K = 100 # number of negative samples C = 3 # nearby words threshold NUM_EPOCHS = 2 # The number of epochs of training MAX_VOCAB_SIZE = 30000 # the vocabulary size BATCH_SIZE = 128 # the batch size LEARNING_RATE = 0.2 # the initial learning rate EMBEDDING_SIZE = 100 LOG_FILE = "word-embedding.log" # tokenize函数，把一篇文本转化成一个个单词 def word_tokenize(text): return text.split() with open("./text8/text8.train.txt", "r") as fin: text = fin.read() text = [w for w in word_tokenize(text.lower())] vocab = dict(Counter(text).most_common(MAX_VOCAB_SIZE-1)) #减一是为了留一个位置给unknow的单词 vocab["<unk>"] = len(text) - np.sum(list(vocab.values())) idx_to_word = [word for word in vocab.keys()] word_to_idx = {word:i for i, word in enumerate(idx_to_word)} word_counts = np.array([count for count in vocab.values()], dtype=np.float32) word_freqs = word_counts / np.sum(word_counts) word_freqs = word_freqs ** (3./4.) #论文中提到的将频率3/4 然后做归一化，对预测准确率有提高 word_freqs = word_freqs / np.sum(word_freqs) # 用来做 negative sampling VOCAB_SIZE = len(idx_to_word) class WordEmbeddingDataset(tud.Dataset): def __init__(self, text, word_to_idx, idx_to_word, word_freqs, word_counts): ''' text: a list of words, all text from the training dataset word_to_idx: the dictionary from word to idx idx_to_word: idx to word mapping word_freq: the frequency of each word word_counts: the word counts ''' super(WordEmbeddingDataset, self).__init__() self.text_encoded = [word_to_idx.get(t, VOCAB_SIZE-1) for t in text] self.text_encoded = torch.Tensor(self.text_encoded).long() self.word_to_idx = word_to_idx self.idx_to_word = idx_to_word self.word_freqs = torch.Tensor(word_freqs) self.word_counts = torch.Tensor(word_counts) def __len__(self): ''' 返回整个数据集（所有单词）的长度 ''' return len(self.text_encoded) def __getitem__(self, idx): ''' 这个function返回以下数据用于训练 - 中心词 - 这个单词附近的(positive)单词 - 随机采样的K个单词作为negative sample ''' center_word = self.text_encoded[idx] pos_indices = list(range(idx-C, idx)) + list(range(idx+1, idx+C+1)) #window内单词的index pos_indices = [i%len(self.text_encoded) for i in pos_indices] #取余防止超出text长度 pos_words = self.text_encoded[pos_indices] #周围单词 neg_words = torch.multinomial(self.word_freqs, K * pos_words.shape[0], True) #负例采样 return center_word, pos_words, neg_words class EmbeddingModel(nn.Module): def __init__(self, vocab_size, embed_size): ''' 初始化输出和输出embedding ''' super(EmbeddingModel, self).__init__() self.vocab_size = vocab_size self.embed_size = embed_size initrange = 0.5 / self.embed_size self.out_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False) self.out_embed.weight.data.uniform_(-initrange, initrange) self.in_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False) self.in_embed.weight.data.uniform_(-initrange, initrange) def forward(self, input_labels, pos_labels, neg_labels): ''' input_labels: 中心词, [batch_size] pos_labels: 中心词周围 context window 出现过的单词 [batch_size * (window_size * 2)] neg_labelss: 中心词周围没有出现过的单词，从 negative sampling 得到 [batch_size, (window_size * 2 * K)] return: loss, [batch_size] ''' batch_size = input_labels.size(0) input_embedding = self.in_embed(input_labels) # B * embed_size pos_embedding = self.out_embed(pos_labels) # B * (2*C) * embed_size neg_embedding = self.out_embed(neg_labels) # B * (2*C * K) * embed_size log_pos = torch.bmm(pos_embedding, input_embedding.unsqueeze(2)).squeeze() # B * (2*C) log_neg = torch.bmm(neg_embedding, -input_embedding.unsqueeze(2)).squeeze() # B * (2*C*K) log_pos = F.logsigmoid(log_pos).sum(1) log_neg = F.logsigmoid(log_neg).sum(1) # batch_size loss = log_pos + log_neg return -loss def input_embeddings(self): return self.in_embed.weight.data.cpu().numpy()

论文地址：http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

词嵌入效果评估：

1. 词类比任务
2. 词相似度任务
3. 作为特征用于CRF实体识别