Merge pull request #574 from kexinzhao/fix_doc

Fixes wrong links to documents in Chinese

README.cn.md

@@ -38,7 +38,7 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
 
 文本分类是自然语言处理领域最基础的任务之一，深度学习方法能够免除复杂的特征工程，直接使用原始文本作为输入，数据驱动地最优化分类准确率。
 
-在文本分类任务中，我们以情感分类任务为例，提供了基于DNN的非序列文本分类模型，以及基于CNN的序列模型供大家学习和使用（基于LSTM的模型见PaddleBook中[情感分类](https://github.com/PaddlePaddle/book/blob/develop/06.understand_sentiment/README.cn.md)一课）。
+在文本分类任务中，我们以情感分类任务为例，提供了基于DNN的非序列文本分类模型，以及基于CNN的序列模型供大家学习和使用（基于LSTM的模型见PaddleBook中[情感分类](http://www.paddlepaddle.org/docs/develop/book/06.understand_sentiment/index.cn.html)一课）。
 
 - 4.1 [基于DNN/CNN的情感分类](https://github.com/PaddlePaddle/models/tree/develop/text_classification)
 - 4.2 [基于双层序列的文本分类模型](https://github.com/PaddlePaddle/models/tree/develop/nested_sequence/text_classification)
@@ -47,7 +47,7 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
 
 排序学习(Learning to Rank， LTR)是信息检索和搜索引擎研究的核心问题之一，通过机器学习方法学习一个分值函数对待排序的候选进行打分，再根据分值的高低确定序关系。深度神经网络可以用来建模分值函数，构成各类基于深度学习的LTR模型。
 
-在排序学习任务中，我们介绍基于RankLoss损失函数Pairwise排序模型和基于LambdaRank损失函数的Listwise排序模型(Pointwise学习策略见PaddleBook中[推荐系统](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)一课）。
+在排序学习任务中，我们介绍基于RankLoss损失函数Pairwise排序模型和基于LambdaRank损失函数的Listwise排序模型(Pointwise学习策略见PaddleBook中[推荐系统](http://www.paddlepaddle.org/docs/develop/book/05.recommender_system/index.cn.html)一课）。
 
 - 5.1 [基于Pairwise和Listwise的排序学习](https://github.com/PaddlePaddle/models/tree/develop/ltr)
 

README.md

@@ -34,7 +34,7 @@ In the example of click-through rate estimates, we first give the Google's Wide
 
 Text classification is one of the most basic tasks in natural language processing. The deep learning method can eliminate the complex feature engineering, and use the original text as input to optimize the classification accuracy.
 
-For text classification, we provide a non-sequential text classification model based on DNN and CNN. (For LSTM-based model, please refer to PaddleBook [Sentiment Analysis](https://github.com/PaddlePaddle/book/blob/develop/06.understand_sentiment/README.cn.md)).
+For text classification, we provide a non-sequential text classification model based on DNN and CNN. (For LSTM-based model, please refer to PaddleBook [Sentiment Analysis](http://www.paddlepaddle.org/docs/develop/book/06.understand_sentiment/index.html)).
 
 - 4.1 [Sentiment analysis based on DNN / CNN](https://github.com/PaddlePaddle/models/tree/develop/text_classification)
 
@@ -43,7 +43,7 @@ For text classification, we provide a non-sequential text classification model b
 Learning to rank (LTR) is one of the core problems in information retrieval and search engine research. Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries.
 The depth neural network can be used to model the fractional function to form various LTR models based on depth learning.
 
-The algorithms for learning to rank are usually categorized into three groups by their input representation and the loss function. These are pointwise, pairwise and listwise approaches. Here we demonstrate RankLoss loss function method (pairwise approach), and LambdaRank loss function method (listwise approach). (For Pointwise approaches, please refer to [Recommended System](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)).
+The algorithms for learning to rank are usually categorized into three groups by their input representation and the loss function. These are pointwise, pairwise and listwise approaches. Here we demonstrate RankLoss loss function method (pairwise approach), and LambdaRank loss function method (listwise approach). (For Pointwise approaches, please refer to [Recommended System](http://www.paddlepaddle.org/docs/develop/book/05.recommender_system/index.html)).
 
 - 5.1 [Learning to rank based on Pairwise and Listwise approches](https://github.com/PaddlePaddle/models/tree/develop/ltr)