diff --git a/word_embedding/README.md b/word_embedding/README.md
index a0990367ef8b03c70c29d285e22ef85907e1d0b7..66798f9a2fe8e7921dd819a444b19183bd70de67 100644
--- a/word_embedding/README.md
+++ b/word_embedding/README.md
@@ -1 +1,163 @@
-TBD
+# Hsigmoid加速词向量训练
+## 背景介绍
+在自然语言处理领域中，传统做法通常使用one-hot向量来表示词，比如词典为['我', '你', '喜欢']，可以用[1,0,0]、[0,1,0]和[0,0,1]这三个向量分别表示'我'、'你'和'喜欢'。这种表示方式比较简洁，但是当词表很大时，容易产生维度爆炸问题；而且任意两个词的向量是正交的，向量包含的信息有限。为了避免或减轻one-hot表示的缺点，目前通常使用词向量来取代one-hot表示，词向量也就是word embedding，即使用一个低维稠密的实向量取代高维稀疏的one-hot向量。训练词向量的方法有很多种，神经网络模型是其中之一，包括CBOW、Skip-gram等，这些模型本质上都是一个分类模型，当词表较大即类别较多时，传统的softmax将非常消耗时间。PaddlePaddle提供了Hsigmoid Layer、NCE Layer，来加速模型的训练过程。本文主要介绍如何使用Hsigmoid Layer来加速训练，词向量相关内容请查阅PaddlePaddle Book中的[词向量章节](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)。
+
+## Hsigmoid Layer
+Hsigmoid Layer引用自论文\[[1](#参考文献)\]，Hsigmoid指Hierarchical-sigmoid，原理是通过构建一个分类二叉树来降低计算复杂度，二叉树中每个叶子节点代表一个类别，每个非叶子节点代表一个二类别分类器。例如我们一共有4个类别分别是0、1、2、3，softmax会分别计算4个类别的得分，然后归一化得到概率。当类别数很多时，计算每个类别的概率非常耗时，Hsigmoid Layer会根据类别数构建一个平衡二叉树，如下：
+
+<p align="center">
+<img src="images/binary_tree.png" width="220" hspace='10'/> <img src="images/path_to_1.png" width="220" hspace='10'/> <br/>
+图1. （a）为平衡二叉树，（b）为根节点到类别1的路径
+</p>
+
+二叉树中每个非叶子节点是一个二类别分类器（sigmoid），如果类别是0，则取左子节点继续分类判断，反之取右子节点，直至达到叶节点。按照这种方式，每个类别均对应一条路径，例如从root到类别1的路径编码为0、1。训练阶段我们按照真实类别对应的路径，依次计算对应分类器的损失，然后综合所有损失得到最终损失。预测阶段，模型会输出各个非叶节点分类器的概率，我们可以根据概率获取路径编码，然后遍历路径编码就可以得到最终预测类别。传统softmax的计算复杂度为N（N为词典大小），Hsigmoid可以将复杂度降至log(N)，详细理论细节可参照论文\[[1](#参考文献)\]。
+
+## 数据准备
+### PTB数据
+本文采用Penn Treebank (PTB)数据集（[Tomas Mikolov预处理版本](http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz)），共包含train、valid和test三个文件。其中使用train作为训练数据，valid作为测试数据。本文训练的是5-gram模型，即用每条数据的前4个词来预测第5个词。PaddlePaddle提供了对应PTB数据集的python包[paddle.dataset.imikolov](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/dataset/imikolov.py)    ，自动做数据的下载与预处理。预处理会把数据集中的每一句话前后加上开始符号\<s>以及结束符号\<e>，然后依据窗口大小（本文为5），从头到尾每次向右滑动窗口并生成一条数据。如"I have a dream that one day"可以生成\<s> I have a dream、I have a dream that、have a dream that one、a dream that one day、dream that one day \<e>，PaddlePaddle会把词转换成id数据作为预处理的输出。
+
+### 自定义数据
+用户可以使用自己的数据集训练模型，自定义数据集最关键的地方是实现reader接口做数据处理，reader需要产生一个迭代器，迭代器负责解析文件中的每一行数据，返回一个python list，例如[1, 2, 3, 4, 5]，分别是第一个到第四个词在字典中的id，PaddlePaddle会进一步将该list转化成`paddle.data_type.inter_value`类型作为data layer的输入，一个封装样例如下：
+
+```python
+def reader_creator(filename, word_dict, n):
+    def reader():
+        with open(filename) as f:
+            UNK = word_dict['<unk>']
+            for l in f:
+                l = ['<s>'] + l.strip().split() + ['<e>']
+                if len(l) >= n:
+                    l = [word_dict.get(w, UNK) for w in l]
+                    for i in range(n, len(l) + 1):
+                        yield tuple(l[i - n:i])
+    return reader
+
+
+def train_data(filename, word_dict, n):
+    """
+    Reader interface for training data.
+
+    It returns a reader creator, each sample in the reader is a word ID tuple.
+
+    :param filename: path of data file
+    :type filename: str
+    :param word_dict: word dictionary
+    :type word_dict: dict
+    :param n: sliding window size
+    :type n: int
+    """
+    return reader_creator(filename, word_dict, n)
+```
+
+## 网络结构
+本文通过训练N-gram语言模型来获得词向量，具体地使用前4个词来预测当前词。网络输入为词在字典中的id，然后查询词向量词表获取词向量，接着拼接4个词的词向量，然后接入一个全连接隐层，最后是Hsigmoid层。详细网络结构见图2：
+
+<p align="center">
+<img src="images/network_conf.png" width = "70%" align="center"/><br/>
+图2. 网络配置结构
+</p>
+
+代码实现如下：
+
+```python
+import math
+import paddle.v2 as paddle
+
+
+def network_conf(hidden_size, embed_size, dict_size, is_train=True):
+    first_word = paddle.layer.data(
+        name='firstw', type=paddle.data_type.integer_value(dict_size))
+    second_word = paddle.layer.data(
+        name='secondw', type=paddle.data_type.integer_value(dict_size))
+    third_word = paddle.layer.data(
+        name='thirdw', type=paddle.data_type.integer_value(dict_size))
+    fourth_word = paddle.layer.data(
+        name='fourthw', type=paddle.data_type.integer_value(dict_size))
+    target_word = paddle.layer.data(
+        name='fifthw', type=paddle.data_type.integer_value(dict_size))
+
+    embed_param_attr = paddle.attr.Param(
+        name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0)
+    embed_first_word = paddle.layer.embedding(
+        input=first_word, size=embed_size, param_attr=embed_param_attr)
+    embed_second_word = paddle.layer.embedding(
+        input=second_word, size=embed_size, param_attr=embed_param_attr)
+    embed_third_word = paddle.layer.embedding(
+        input=third_word, size=embed_size, param_attr=embed_param_attr)
+    embed_fourth_word = paddle.layer.embedding(
+        input=fourth_word, size=embed_size, param_attr=embed_param_attr)
+
+    embed_context = paddle.layer.concat(input=[
+        embed_first_word, embed_second_word, embed_third_word, embed_fourth_word
+    ])
+
+    hidden_layer = paddle.layer.fc(
+        input=embed_context,
+        size=hidden_size,
+                act=paddle.activation.Sigmoid(),
+        layer_attr=paddle.attr.Extra(drop_rate=0.5),
+        bias_attr=paddle.attr.Param(learning_rate=2),
+        param_attr=paddle.attr.Param(
+            initial_std=1. / math.sqrt(embed_size * 8), learning_rate=1))
+
+    if is_train == True:
+        cost = paddle.layer.hsigmoid(
+            input=hidden_layer,
+            label=target_word,
+            num_classes=dict_size,
+            param_attr=paddle.attr.Param(name='sigmoid_w'),
+            bias_attr=paddle.attr.Param(name='sigmoid_b'))
+        return cost
+    else:
+        with paddle.layer.mixed(
+                size=dict_size - 1,
+                act=paddle.activation.Sigmoid(),
+                bias_attr=paddle.attr.Param(name='sigmoid_b')) as prediction:
+            prediction += paddle.layer.trans_full_matrix_projection(
+                input=hidden_layer,
+                param_attr=paddle.attr.Param(name='sigmoid_w'))
+        return prediction
+```
+
+需要注意，在预测阶段，我们需要对hsigmoid参数做一次转置，这里输出的类别数为词典大小减1，对应非叶节点的数量。
+
+## 训练阶段
+训练比较简单，直接运行``` python hsigmoid_train.py ```。程序第一次运行会检测用户缓存文件夹中是否包含imikolov数据集，如果未包含，则自动下载。运行过程中，每100个iteration会打印模型训练信息，主要包含训练损失和测试损失，每个pass会保存一次模型。
+
+## 预测阶段
+预测时，直接运行``` python hsigmoid_predict.py ```，程序会首先load模型，然后按照batch方式进行预测，并打印预测结果。预测阶段最重要的就是根据概率得到编码路径，然后遍历路径获取最终的预测类别，这部分逻辑如下：
+
+```python
+def decode_res(infer_res, dict_size):
+    """
+    Inferring probabilities are orginized as a complete binary tree.
+    The actual labels are leaves (indices are counted from class number).
+    This function travels paths decoded from inferring results.
+    If the probability >0.5 then go to right child, otherwise go to left child.
+
+    param infer_res: inferring result
+    param dict_size: class number
+    return predict_lbls: actual class
+    """
+    predict_lbls = []
+    infer_res = infer_res > 0.5
+    for i, probs in enumerate(infer_res):
+        idx = 0
+        result = 1
+        while idx < len(probs):
+            result <<= 1
+            if probs[idx]:
+                result |= 1
+            if probs[idx]:
+                idx = idx * 2 + 2  # right child
+            else:
+                idx = idx * 2 + 1  # left child
+
+        predict_lbl = result - dict_size
+        predict_lbls.append(predict_lbl)
+    return predict_lbls
+```
+
+预测程序的输入数据格式与训练阶段相同，如have a dream that one，程序会根据have a dream that生成一组概率，通过对概率解码生成预测词，one作为真实词，方便评估。解码函数的输入是一个batch样本的预测概率以及词表的大小，里面的循环是对每条样本的输出概率进行解码，解码方式就是按照左0右1的准则，不断遍历路径，直至到达叶子节点。需要注意的是，本文选用的数据集需要较长的时间训练才能得到较好的结果，预测程序选用第一轮的模型，仅为展示方便，学习效果不能保证。
+## 参考文献
+1. Morin, F., & Bengio, Y. (2005, January). [Hierarchical Probabilistic Neural Network Language Model](http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf). In Aistats (Vol. 5, pp. 246-252).
diff --git a/word_embedding/hsigmoid_conf.py b/word_embedding/hsigmoid_conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..be6b7462a1487e906278fa2682d65add256aaa2d
--- /dev/null
+++ b/word_embedding/hsigmoid_conf.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import math
+import paddle.v2 as paddle
+
+
+def network_conf(hidden_size, embed_size, dict_size, is_train=True):
+    first_word = paddle.layer.data(
+        name='firstw', type=paddle.data_type.integer_value(dict_size))
+    second_word = paddle.layer.data(
+        name='secondw', type=paddle.data_type.integer_value(dict_size))
+    third_word = paddle.layer.data(
+        name='thirdw', type=paddle.data_type.integer_value(dict_size))
+    fourth_word = paddle.layer.data(
+        name='fourthw', type=paddle.data_type.integer_value(dict_size))
+    target_word = paddle.layer.data(
+        name='fifthw', type=paddle.data_type.integer_value(dict_size))
+
+    embed_param_attr = paddle.attr.Param(
+        name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0)
+    embed_first_word = paddle.layer.embedding(
+        input=first_word, size=embed_size, param_attr=embed_param_attr)
+    embed_second_word = paddle.layer.embedding(
+        input=second_word, size=embed_size, param_attr=embed_param_attr)
+    embed_third_word = paddle.layer.embedding(
+        input=third_word, size=embed_size, param_attr=embed_param_attr)
+    embed_fourth_word = paddle.layer.embedding(
+        input=fourth_word, size=embed_size, param_attr=embed_param_attr)
+
+    embed_context = paddle.layer.concat(input=[
+        embed_first_word, embed_second_word, embed_third_word, embed_fourth_word
+    ])
+
+    hidden_layer = paddle.layer.fc(
+        input=embed_context,
+        size=hidden_size,
+        act=paddle.activation.Sigmoid(),
+        layer_attr=paddle.attr.Extra(drop_rate=0.5),
+        bias_attr=paddle.attr.Param(learning_rate=2),
+        param_attr=paddle.attr.Param(
+            initial_std=1. / math.sqrt(embed_size * 8), learning_rate=1))
+
+    if is_train == True:
+        cost = paddle.layer.hsigmoid(
+            input=hidden_layer,
+            label=target_word,
+            num_classes=dict_size,
+            param_attr=paddle.attr.Param(name='sigmoid_w'),
+            bias_attr=paddle.attr.Param(name='sigmoid_b'))
+        return cost
+    else:
+        with paddle.layer.mixed(
+                size=dict_size - 1,
+                act=paddle.activation.Sigmoid(),
+                bias_attr=paddle.attr.Param(name='sigmoid_b')) as prediction:
+            prediction += paddle.layer.trans_full_matrix_projection(
+                input=hidden_layer,
+                param_attr=paddle.attr.Param(name='sigmoid_w'))
+        return prediction
diff --git a/word_embedding/hsigmoid_predict.py b/word_embedding/hsigmoid_predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..210f87ee103a2ac145e3c42cea536cd00d2994bb
--- /dev/null
+++ b/word_embedding/hsigmoid_predict.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import paddle.v2 as paddle
+from hsigmoid_conf import network_conf
+import gzip
+
+
+def decode_res(infer_res, dict_size):
+    """
+    Inferring probabilities are orginized as a complete binary tree.
+    The actual labels are leaves (indices are counted from class number).
+    This function travels paths decoded from inferring results.
+    If the probability >0.5 then go to right child, otherwise go to left child.
+
+    param infer_res: inferring result
+    param dict_size: class number
+    return predict_lbls: actual class
+    """
+    predict_lbls = []
+    infer_res = infer_res > 0.5
+    for i, probs in enumerate(infer_res):
+        idx = 0
+        result = 1
+        while idx < len(probs):
+            result <<= 1
+            if probs[idx]:
+                result |= 1
+            if probs[idx]:
+                idx = idx * 2 + 2  # right child
+            else:
+                idx = idx * 2 + 1  # left child
+
+        predict_lbl = result - dict_size
+        predict_lbls.append(predict_lbl)
+    return predict_lbls
+
+
+def predict(batch_ins, idx_word_dict, dict_size, prediction_layer, parameters):
+    infer_res = paddle.infer(
+        output_layer=prediction_layer, parameters=parameters, input=batch_ins)
+
+    predict_lbls = decode_res(infer_res, dict_size)
+    predict_words = [idx_word_dict[lbl] for lbl in predict_lbls]  # map to word
+
+    # Ouput format: word1 word2 word3 word4 -> predict label
+    for i, ins in enumerate(batch_ins):
+        print(idx_word_dict[ins[0]] + ' ' + \
+            idx_word_dict[ins[1]] + ' ' + \
+            idx_word_dict[ins[2]] + ' ' + \
+            idx_word_dict[ins[3]] + ' ' + \
+         ' -> ' + predict_words[i])
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+    word_dict = paddle.dataset.imikolov.build_dict(min_word_freq=2)
+    dict_size = len(word_dict)
+    prediction_layer = network_conf(
+        is_train=False, hidden_size=256, embed_size=32, dict_size=dict_size)
+
+    with gzip.open('./models/model_pass_00000.tar.gz') as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+
+    idx_word_dict = dict((v, k) for k, v in word_dict.items())
+    batch_size = 64
+    batch_ins = []
+    ins_iter = paddle.dataset.imikolov.test(word_dict, 5)
+
+    for ins in ins_iter():
+        batch_ins.append(ins[:-1])
+        if len(batch_ins) == batch_size:
+            predict(batch_ins, idx_word_dict, dict_size, prediction_layer,
+                    parameters)
+            batch_ins = []
+
+    if len(batch_ins) > 0:
+        predict(batch_ins, idx_word_dict, dict_size, prediction_layer,
+                parameters)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/word_embedding/hsigmoid_train.py b/word_embedding/hsigmoid_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c2e1b236b284c3dfb32988b0d917eb830f365be
--- /dev/null
+++ b/word_embedding/hsigmoid_train.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import paddle.v2 as paddle
+from hsigmoid_conf import network_conf
+import gzip
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+    word_dict = paddle.dataset.imikolov.build_dict(min_word_freq=2)
+    dict_size = len(word_dict)
+    cost = network_conf(
+        is_train=True, hidden_size=256, embed_size=32, dict_size=dict_size)
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndPass):
+            model_name = './models/model_pass_%05d.tar.gz' % event.pass_id
+            print("Save model into %s ..." % model_name)
+            with gzip.open(model_name, 'w') as f:
+                parameters.to_tar(f)
+
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                result = trainer.test(
+                    paddle.batch(
+                        paddle.dataset.imikolov.test(word_dict, 5), 32))
+                print("Pass %d, Batch %d, Cost %f, Test Cost %f" %
+                      (event.pass_id, event.batch_id, event.cost, result.cost))
+
+    feeding = {
+        'firstw': 0,
+        'secondw': 1,
+        'thirdw': 2,
+        'fourthw': 3,
+        'fifthw': 4
+    }
+
+    parameters = paddle.parameters.create(cost)
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+
+    trainer.train(
+        paddle.batch(
+            paddle.reader.shuffle(
+                lambda: paddle.dataset.imikolov.train(word_dict, 5)(),
+                buf_size=1000), 64),
+        num_passes=30,
+        event_handler=event_handler,
+        feeding=feeding)
+
+
+if __name__ == '__main__':
+    main()
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