Fixing anchors (#430)

* Fixes anchor issues, except for ch1.

* Remove old files.

03.image_classification/README.md

@@ -522,7 +522,7 @@ print "Label of image/dog.png is: %d" % lab[0][0]
 Traditional image classification methods involve multiple stages of processing, which has to utilize complex frameworks. Contrarily, CNN models can be trained end-to-end with a significant increase in classification accuracy. In this chapter, we introduced three models -- VGG, GoogleNet, ResNet and provided PaddlePaddle config files for training VGG and ResNet on CIFAR10. We also explained how to perform prediction and feature extraction using the PaddlePaddle API. For other datasets such as ImageNet, the procedure for config and training are the same and you are welcome to give it a try.
 
 
-## Reference
+## References
 
 [1] D. G. Lowe, [Distinctive image features from scale-invariant keypoints](http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf). IJCV, 60(2):91-110, 2004.
 

03.image_classification/index.html

@@ -564,7 +564,7 @@ print "Label of image/dog.png is: %d" % lab[0][0]
 Traditional image classification methods involve multiple stages of processing, which has to utilize complex frameworks. Contrarily, CNN models can be trained end-to-end with a significant increase in classification accuracy. In this chapter, we introduced three models -- VGG, GoogleNet, ResNet and provided PaddlePaddle config files for training VGG and ResNet on CIFAR10. We also explained how to perform prediction and feature extraction using the PaddlePaddle API. For other datasets such as ImageNet, the procedure for config and training are the same and you are welcome to give it a try.
 
 
-## Reference
+## References
 
 [1] D. G. Lowe, [Distinctive image features from scale-invariant keypoints](http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf). IJCV, 60(2):91-110, 2004.
 

04.word2vec/README.md

@@ -18,7 +18,7 @@ One-hot vectors are intuitive, yet they have limited usefulness. Take the exampl
 
 Like many machine learning models, word embeddings can represent knowledge in various ways. Another model may project an one-hot vector to an embedding vector of lower dimension e.g. $embedding(mother's day) = [0.3, 4.2, -1.5, ...], embedding(carnations) = [0.2, 5.6, -2.3, ...]$. Mapping one-hot vectors onto an embedded vector space has the potential to bring the embedding vectors of similar words (either semantically or usage-wise) closer to each other, so that the cosine similarity between the corresponding vectors for words like "Mother's Day" and "carnations" are no longer zero.
 
-A word embedding model could be a probabilistic model, a co-occurrence matrix model, or a neural network. Before people started using neural networks to generate word embedding, the traditional method was to calculate a co-occurrence matrix $X$ of words. Here, $X$ is a $|V| \times |V|$ matrix, where $X_{ij}$ represents the co-occurrence times of the $i$th and $j$th words in the vocabulary `V` within all corpus, and $|V|$ is the size of the vocabulary. By performing matrix decomposition on $X$ e.g. Singular Value Decomposition \[[5](#References)\]
+A word embedding model could be a probabilistic model, a co-occurrence matrix model, or a neural network. Before people started using neural networks to generate word embedding, the traditional method was to calculate a co-occurrence matrix $X$ of words. Here, $X$ is a $|V| \times |V|$ matrix, where $X_{ij}$ represents the co-occurrence times of the $i$th and $j$th words in the vocabulary `V` within all corpus, and $|V|$ is the size of the vocabulary. By performing matrix decomposition on $X$ e.g. Singular Value Decomposition \[[5](#references)\]
 
 $$X = USV^T$$
 
@@ -33,7 +33,7 @@ The neural network based model does not require storing huge hash tables of stat
 
 ## Results Demonstration
 
-In this section, we use the $t-$SNE\[[4](#reference)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
+In this section, we use the $t-$SNE\[[4](#references)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
 
 <p align="center">
     <img src = "image/2d_similarity.png" width=400><br/>
@@ -61,7 +61,7 @@ In this section, we will introduce three word embedding models: N-gram model, CB
 
 For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Training](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
 
-The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#reference)\]. Despite their apparent simplicity, these models train very well.
+The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#references)\]. Despite their apparent simplicity, these models train very well.
 
 ### Language Model
 
@@ -83,7 +83,7 @@ $$P(w_1, ..., w_T) = \prod_{t=1}^TP(w_t | w_1, ... , w_{t-1})$$
 
 In computational linguistics, n-gram is an important method to represent text. An n-gram represents a contiguous sequence of n consecutive items given a text. Based on the desired application scenario, each item could be a letter, a syllable or a word. The N-gram model is also an important method in statistical language modeling. When training language models with n-grams, the first (n-1) words of an n-gram are used to predict the *n*th word.
 
-Yoshua Bengio and other scientists describe how to train a word embedding model using neural network in the famous paper of Neural Probabilistic Language Models \[[1](#reference)\] published in 2003. The Neural Network Language Model (NNLM) described in the paper learns the language model and word embedding simultaneously through a linear transformation and a non-linear hidden connection. That is, after training on large amounts of corpus, the model learns the word embedding; then, it computes the probability of the whole sentence, using the embedding. This type of language model can overcome the **curse of dimensionality** i.e. model inaccuracy caused by the difference in dimensionality between training and testing data. Note that the term *neural network language model* is ill-defined, so we will not use the name NNLM but only refer to it as *N-gram neural model* in this section.
+Yoshua Bengio and other scientists describe how to train a word embedding model using neural network in the famous paper of Neural Probabilistic Language Models \[[1](#references)\] published in 2003. The Neural Network Language Model (NNLM) described in the paper learns the language model and word embedding simultaneously through a linear transformation and a non-linear hidden connection. That is, after training on large amounts of corpus, the model learns the word embedding; then, it computes the probability of the whole sentence, using the embedding. This type of language model can overcome the **curse of dimensionality** i.e. model inaccuracy caused by the difference in dimensionality between training and testing data. Note that the term *neural network language model* is ill-defined, so we will not use the name NNLM but only refer to it as *N-gram neural model* in this section.
 
 We have previously described language model using conditional probability, where the probability of the *t*-th word in a sentence depends on all $t-1$ words before it. Furthermore, since words further prior have less impact on a word, and every word within an n-gram is only effected by its previous n-1 words, we have:
 
@@ -151,7 +151,7 @@ As illustrated in the figure above, skip-gram model maps the word embedding of t
 
 ## Dataset
 
-We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
+We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#references)\]. Its statistics are as follows:
 
 <p align="center">
 <table>
@@ -439,7 +439,7 @@ This chapter introduces word embeddings, the relationship between language model
 In information retrieval, the relevance between the query and document keyword can be computed through the cosine similarity of their word embeddings. In grammar analysis and semantic analysis, a previously trained word embedding can initialize models for better performance. In document classification, clustering the word embedding can group synonyms in the documents. We hope that readers can use word embedding models in their work after reading this chapter.
 
 
-## Referenes
+## References
 1. Bengio Y, Ducharme R, Vincent P, et al. [A neural probabilistic language model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)[J]. journal of machine learning research, 2003, 3(Feb): 1137-1155.
 2. Mikolov T, Kombrink S, Deoras A, et al. [Rnnlm-recurrent neural network language modeling toolkit](http://www.fit.vutbr.cz/~imikolov/rnnlm/rnnlm-demo.pdf)[C]//Proc. of the 2011 ASRU Workshop. 2011: 196-201.
 3. Mikolov T, Chen K, Corrado G, et al. [Efficient estimation of word representations in vector space](https://arxiv.org/pdf/1301.3781.pdf)[J]. arXiv preprint arXiv:1301.3781, 2013.

04.word2vec/index.html

@@ -60,7 +60,7 @@ One-hot vectors are intuitive, yet they have limited usefulness. Take the exampl
 
 Like many machine learning models, word embeddings can represent knowledge in various ways. Another model may project an one-hot vector to an embedding vector of lower dimension e.g. $embedding(mother's day) = [0.3, 4.2, -1.5, ...], embedding(carnations) = [0.2, 5.6, -2.3, ...]$. Mapping one-hot vectors onto an embedded vector space has the potential to bring the embedding vectors of similar words (either semantically or usage-wise) closer to each other, so that the cosine similarity between the corresponding vectors for words like "Mother's Day" and "carnations" are no longer zero.
 
-A word embedding model could be a probabilistic model, a co-occurrence matrix model, or a neural network. Before people started using neural networks to generate word embedding, the traditional method was to calculate a co-occurrence matrix $X$ of words. Here, $X$ is a $|V| \times |V|$ matrix, where $X_{ij}$ represents the co-occurrence times of the $i$th and $j$th words in the vocabulary `V` within all corpus, and $|V|$ is the size of the vocabulary. By performing matrix decomposition on $X$ e.g. Singular Value Decomposition \[[5](#References)\]
+A word embedding model could be a probabilistic model, a co-occurrence matrix model, or a neural network. Before people started using neural networks to generate word embedding, the traditional method was to calculate a co-occurrence matrix $X$ of words. Here, $X$ is a $|V| \times |V|$ matrix, where $X_{ij}$ represents the co-occurrence times of the $i$th and $j$th words in the vocabulary `V` within all corpus, and $|V|$ is the size of the vocabulary. By performing matrix decomposition on $X$ e.g. Singular Value Decomposition \[[5](#references)\]
 
 $$X = USV^T$$
 
@@ -75,7 +75,7 @@ The neural network based model does not require storing huge hash tables of stat
 
 ## Results Demonstration
 
-In this section, we use the $t-$SNE\[[4](#reference)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
+In this section, we use the $t-$SNE\[[4](#references)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
 
 <p align="center">
     <img src = "image/2d_similarity.png" width=400><br/>
@@ -103,7 +103,7 @@ In this section, we will introduce three word embedding models: N-gram model, CB
 
 For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Training](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
 
-The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#reference)\]. Despite their apparent simplicity, these models train very well.
+The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#references)\]. Despite their apparent simplicity, these models train very well.
 
 ### Language Model
 
@@ -125,7 +125,7 @@ $$P(w_1, ..., w_T) = \prod_{t=1}^TP(w_t | w_1, ... , w_{t-1})$$
 
 In computational linguistics, n-gram is an important method to represent text. An n-gram represents a contiguous sequence of n consecutive items given a text. Based on the desired application scenario, each item could be a letter, a syllable or a word. The N-gram model is also an important method in statistical language modeling. When training language models with n-grams, the first (n-1) words of an n-gram are used to predict the *n*th word.
 
-Yoshua Bengio and other scientists describe how to train a word embedding model using neural network in the famous paper of Neural Probabilistic Language Models \[[1](#reference)\] published in 2003. The Neural Network Language Model (NNLM) described in the paper learns the language model and word embedding simultaneously through a linear transformation and a non-linear hidden connection. That is, after training on large amounts of corpus, the model learns the word embedding; then, it computes the probability of the whole sentence, using the embedding. This type of language model can overcome the **curse of dimensionality** i.e. model inaccuracy caused by the difference in dimensionality between training and testing data. Note that the term *neural network language model* is ill-defined, so we will not use the name NNLM but only refer to it as *N-gram neural model* in this section.
+Yoshua Bengio and other scientists describe how to train a word embedding model using neural network in the famous paper of Neural Probabilistic Language Models \[[1](#references)\] published in 2003. The Neural Network Language Model (NNLM) described in the paper learns the language model and word embedding simultaneously through a linear transformation and a non-linear hidden connection. That is, after training on large amounts of corpus, the model learns the word embedding; then, it computes the probability of the whole sentence, using the embedding. This type of language model can overcome the **curse of dimensionality** i.e. model inaccuracy caused by the difference in dimensionality between training and testing data. Note that the term *neural network language model* is ill-defined, so we will not use the name NNLM but only refer to it as *N-gram neural model* in this section.
 
 We have previously described language model using conditional probability, where the probability of the *t*-th word in a sentence depends on all $t-1$ words before it. Furthermore, since words further prior have less impact on a word, and every word within an n-gram is only effected by its previous n-1 words, we have:
 
@@ -193,7 +193,7 @@ As illustrated in the figure above, skip-gram model maps the word embedding of t
 
 ## Dataset
 
-We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
+We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#references)\]. Its statistics are as follows:
 
 <p align="center">
 <table>
@@ -481,7 +481,7 @@ This chapter introduces word embeddings, the relationship between language model
 In information retrieval, the relevance between the query and document keyword can be computed through the cosine similarity of their word embeddings. In grammar analysis and semantic analysis, a previously trained word embedding can initialize models for better performance. In document classification, clustering the word embedding can group synonyms in the documents. We hope that readers can use word embedding models in their work after reading this chapter.
 
 
-## Referenes
+## References
 1. Bengio Y, Ducharme R, Vincent P, et al. [A neural probabilistic language model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)[J]. journal of machine learning research, 2003, 3(Feb): 1137-1155.
 2. Mikolov T, Kombrink S, Deoras A, et al. [Rnnlm-recurrent neural network language modeling toolkit](http://www.fit.vutbr.cz/~imikolov/rnnlm/rnnlm-demo.pdf)[C]//Proc. of the 2011 ASRU Workshop. 2011: 196-201.
 3. Mikolov T, Chen K, Corrado G, et al. [Efficient estimation of word representations in vector space](https://arxiv.org/pdf/1301.3781.pdf)[J]. arXiv preprint arXiv:1301.3781, 2013.

05.recommender_system/README.md

@@ -7,18 +7,18 @@ The source code from this tutorial is at [here](https://github.com/PaddlePaddle/
 
 The recommender system is a component of e-commerce, online videos, and online reading services.  There are several different approaches for recommender systems to learn from user behavior and product properties and to understand users' interests.
 
-- User behavior-based approach.  A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
+- User behavior-based approach.  A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#references)], item-based [[4](#references)], social network based[[5](#references)], and model-based.
 
-- Content-based approach[[1](#reference)].  This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
+- Content-based approach[[1](#references)].  This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
 
-- Hybrid approach[[2](#reference)]: This one combines above two to help with each other about the data sparsity problem[[6](#reference)].
+- Hybrid approach[[2](#references)]: This one combines above two to help with each other about the data sparsity problem[[6](#references)].
 
 This tutorial explains a deep learning based hybrid approach and its implement in PaddlePaddle.  We are going to train a model using a dataset that includes user information, movie information, and ratings.  Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
 
 
 ## Model Overview
 
-To know more about deep learning based recommendation, let us start from going over the Youtube recommender system[[7](#reference)] before introducing our hybrid model.
+To know more about deep learning based recommendation, let us start from going over the Youtube recommender system[[7](#references)] before introducing our hybrid model.
 
 
 ### YouTube's Deep Learning Recommendation Model
@@ -32,14 +32,14 @@ Figure 1. YouTube recommender system overview.
 
 #### Candidate Generation Network
 
-Youtube models candidate generation as a multiclass classification problem with a huge number of classes equal to the number of videos.  The architecture of the model is as follows:
+YouTube models candidate generation as a multi-class classification problem with a huge number of classes equal to the number of videos.  The architecture of the model is as follows:
 
 <p align="center">
 <img src="image/Deep_candidate_generation_model_architecture.en.png" width="70%" ><br/>
 Figure 2. Deep candidate generation model.
 </p>
 
-The first stage of this model maps watching history and search queries into fixed-length representative features.  Then, an MLP (multi-layer perceptron, as described in the [Recognize Digits](https://github.com/PaddlePaddle/book/blob/develop/recognize_digits/README.md) tutorial) takes the concatenation of all representative vectors.  The output of the MLP represents the user' *intrinsic interests*.  At training time, it is used together with a softmax output layer for minimizing the classification error.   At serving time, it is used to compute the relevance of the user with all movies.
+The first stage of this model maps watching history and search queries into fixed-length representative features.  Then, an MLP (multi-layer Perceptron, as described in the [Recognize Digits](https://github.com/PaddlePaddle/book/blob/develop/recognize_digits/README.md) tutorial) takes the concatenation of all representative vectors.  The output of the MLP represents the user' *intrinsic interests*.  At training time, it is used together with a softmax output layer for minimizing the classification error.   At serving time, it is used to compute the relevance of the user with all movies.
 
 For a user $U$, the predicted watching probability of video $i$ is
 
@@ -378,7 +378,7 @@ trainer.train(
 
 This tutorial goes over traditional approaches in recommender system and a deep learning based approach.  We also show that how to train and use the model with PaddlePaddle.  Deep learning has been well used in computer vision and NLP, we look forward to its new successes in recommender systems.
 
-## Reference
+## References
 
 1. [Peter Brusilovsky](https://en.wikipedia.org/wiki/Peter_Brusilovsky) (2007). *The Adaptive Web*. p. 325.
 2. Robin Burke , [Hybrid Web Recommender Systems](http://www.dcs.warwick.ac.uk/~acristea/courses/CS411/2010/Book%20-%20The%20Adaptive%20Web/HybridWebRecommenderSystems.pdf), pp. 377-408, The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (Ed.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, Vol. 4321, May 2007, 978-3-540-72078-2.

05.recommender_system/index.html

@@ -49,18 +49,18 @@ The source code from this tutorial is at [here](https://github.com/PaddlePaddle/
 
 The recommender system is a component of e-commerce, online videos, and online reading services.  There are several different approaches for recommender systems to learn from user behavior and product properties and to understand users' interests.
 
-- User behavior-based approach.  A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
+- User behavior-based approach.  A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#references)], item-based [[4](#references)], social network based[[5](#references)], and model-based.
 
-- Content-based approach[[1](#reference)].  This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
+- Content-based approach[[1](#references)].  This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
 
-- Hybrid approach[[2](#reference)]: This one combines above two to help with each other about the data sparsity problem[[6](#reference)].
+- Hybrid approach[[2](#references)]: This one combines above two to help with each other about the data sparsity problem[[6](#references)].
 
 This tutorial explains a deep learning based hybrid approach and its implement in PaddlePaddle.  We are going to train a model using a dataset that includes user information, movie information, and ratings.  Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
 
 
 ## Model Overview
 
-To know more about deep learning based recommendation, let us start from going over the Youtube recommender system[[7](#reference)] before introducing our hybrid model.
+To know more about deep learning based recommendation, let us start from going over the Youtube recommender system[[7](#references)] before introducing our hybrid model.
 
 
 ### YouTube's Deep Learning Recommendation Model
@@ -74,14 +74,14 @@ Figure 1. YouTube recommender system overview.
 
 #### Candidate Generation Network
 
-Youtube models candidate generation as a multiclass classification problem with a huge number of classes equal to the number of videos.  The architecture of the model is as follows:
+YouTube models candidate generation as a multi-class classification problem with a huge number of classes equal to the number of videos.  The architecture of the model is as follows:
 
 <p align="center">
 <img src="image/Deep_candidate_generation_model_architecture.en.png" width="70%" ><br/>
 Figure 2. Deep candidate generation model.
 </p>
 
-The first stage of this model maps watching history and search queries into fixed-length representative features.  Then, an MLP (multi-layer perceptron, as described in the [Recognize Digits](https://github.com/PaddlePaddle/book/blob/develop/recognize_digits/README.md) tutorial) takes the concatenation of all representative vectors.  The output of the MLP represents the user' *intrinsic interests*.  At training time, it is used together with a softmax output layer for minimizing the classification error.   At serving time, it is used to compute the relevance of the user with all movies.
+The first stage of this model maps watching history and search queries into fixed-length representative features.  Then, an MLP (multi-layer Perceptron, as described in the [Recognize Digits](https://github.com/PaddlePaddle/book/blob/develop/recognize_digits/README.md) tutorial) takes the concatenation of all representative vectors.  The output of the MLP represents the user' *intrinsic interests*.  At training time, it is used together with a softmax output layer for minimizing the classification error.   At serving time, it is used to compute the relevance of the user with all movies.
 
 For a user $U$, the predicted watching probability of video $i$ is
 
@@ -420,7 +420,7 @@ trainer.train(
 
 This tutorial goes over traditional approaches in recommender system and a deep learning based approach.  We also show that how to train and use the model with PaddlePaddle.  Deep learning has been well used in computer vision and NLP, we look forward to its new successes in recommender systems.
 
-## Reference
+## References
 
 1. [Peter Brusilovsky](https://en.wikipedia.org/wiki/Peter_Brusilovsky) (2007). *The Adaptive Web*. p. 325.
 2. Robin Burke , [Hybrid Web Recommender Systems](http://www.dcs.warwick.ac.uk/~acristea/courses/CS411/2010/Book%20-%20The%20Adaptive%20Web/HybridWebRecommenderSystems.pdf), pp. 377-408, The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (Ed.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, Vol. 4321, May 2007, 978-3-540-72078-2.

06.understand_sentiment/README.md

@@ -19,7 +19,7 @@ In natural language processing, sentiment analysis can be categorized as a **Tex
 
 The BOW model does not capture all the information in a piece of text, as it ignores syntax and grammar and just treats the text as a set of words. For example, “this movie is extremely bad“ and “boring, dull, and empty work” describe very similar semantic meaning, yet their BOW representations have very little similarity. Furthermore, “the movie is bad“ and “the movie is not bad“ have high similarity with BOW features, but they express completely opposite semantics.
 
-This chapter introduces a deep learning model that handles these issues in BOW. Our model embeds texts into a low-dimensional space and takes word order into consideration. It is an end-to-end framework and it has large performance improvement over traditional methods \[[1](#Reference)\].
+This chapter introduces a deep learning model that handles these issues in BOW. Our model embeds texts into a low-dimensional space and takes word order into consideration. It is an end-to-end framework and it has large performance improvement over traditional methods \[[1](#references)\].
 
 ## Model Overview
 
@@ -32,11 +32,11 @@ The convolutional neural network for texts is introduced in chapter [recommender
 
 CNN mainly contains convolution and pooling operation, with versatile combinations in various applications. We firstly apply the convolution operation: we apply the kernel in each window, extracting features. Convolving by the kernel at every window produces a feature map. Next, we apply *max pooling* over time to represent the whole sentence, which is the maximum element across the feature map. In real applications, we will apply multiple CNN kernels on the sentences. It can be implemented efficiently by concatenating the kernels together as a matrix. Also, we can use CNN kernels with different kernel size. Finally, concatenating the resulting features produces a fixed-length representation, which can be combined with a softmax to form the model for the sentiment analysis problem.
 
-For short texts, the aforementioned CNN model can achieve very high accuracy \[[1](#Reference)\]. If we want to extract more abstract representations, we may apply a deeper CNN model \[[2](#Reference),[3](#Reference)\].
+For short texts, the aforementioned CNN model can achieve very high accuracy \[[1](#references)\]. If we want to extract more abstract representations, we may apply a deeper CNN model \[[2](#references),[3](#references)\].
 
 ### Recurrent Neural Network (RNN)
 
-RNN is an effective model for sequential data. In terms of computability, the RNN is Turing-complete \[[4](#Reference)\]. Since NLP is a classical problem of sequential data, the RNN, especially its variant LSTM\[[5](#Reference)\]), achieves state-of-the-art performance on various NLP tasks, such as language modeling, syntax parsing, POS-tagging, image captioning, dialog, machine translation, and so forth.
+RNN is an effective model for sequential data. In terms of computability, the RNN is Turing-complete \[[4](#references)\]. Since NLP is a classical problem of sequential data, the RNN, especially its variant LSTM\[[5](#references)\]), achieves state-of-the-art performance on various NLP tasks, such as language modeling, syntax parsing, POS-tagging, image captioning, dialog, machine translation, and so forth.
 
 <p align="center">
 <img src="image/rnn.png" width = "60%" align="center"/><br/>
@@ -53,13 +53,13 @@ In NLP, words are often represented as one-hot vectors and then mapped to an emb
 
 ### Long-Short Term Memory (LSTM)
 
-Training an RNN on long sequential data sometimes leads to the gradient vanishing or exploding\[[6](#)\]. To solve this problem Hochreiter S, Schmidhuber J. (1997) proposed **Long Short Term Memory** (LSTM)\[[5](#Reference)\]).
+Training an RNN on long sequential data sometimes leads to the gradient vanishing or exploding\[[6](#references)\]. To solve this problem Hochreiter S, Schmidhuber J. (1997) proposed **Long Short Term Memory** (LSTM)\[[5](#references)\]).
 
 Compared to the structure of a simple RNN, an LSTM includes memory cell $c$, input gate $i$, forget gate $f$ and output gate $o$. These gates and memory cells dramatically improve the ability for the network to handle long sequences. We can formulate the **LSTM-RNN**, denoted as a function $F$, as follows：
 
 $$ h_t=F(x_t,h_{t-1})$$
 
-$F$ contains following formulations\[[7](#Reference)\]：
+$F$ contains following formulations\[[7](#references)\]：
 \begin{align}
 i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
 f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
@@ -82,7 +82,7 @@ where $Recrurent$ is a simple RNN, GRU or LSTM.
 
 ### Stacked Bidirectional LSTM
 
-For vanilla LSTM, $h_t$ contains input information from previous time-step $1..t-1$ context. We can also apply an RNN with reverse-direction to take successive context $t+1…n$ into consideration. Combining constructing deep RNN (deeper RNN can contain more abstract and higher level semantic), we can design structures with deep stacked bidirectional LSTM to model sequential data\[[9](#Reference)\].
+For vanilla LSTM, $h_t$ contains input information from previous time-step $1..t-1$ context. We can also apply an RNN with reverse-direction to take successive context $t+1…n$ into consideration. Combining constructing deep RNN (deeper RNN can contain more abstract and higher level semantic), we can design structures with deep stacked bidirectional LSTM to model sequential data\[[9](#references)\].
 
 As shown in Figure 3 (3-layer RNN), odd/even layers are forward/reverse LSTM. Higher layers of LSTM take lower-layers LSTM as input, and the top-layer LSTM produces a fixed length vector by max-pooling (this representation considers contexts from previous and successive words for higher-level abstractions). Finally, we concatenate the output to a softmax layer for classification.
 
@@ -331,7 +331,7 @@ trainer.train(
 
 In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters, we will see how these models can be applied in other tasks.
 
-## Reference
+## References
 
 1. Kim Y. [Convolutional neural networks for sentence classification](http://arxiv.org/pdf/1408.5882)[J]. arXiv preprint arXiv:1408.5882, 2014.
 2. Kalchbrenner N, Grefenstette E, Blunsom P. [A convolutional neural network for modeling sentences](http://arxiv.org/pdf/1404.2188.pdf?utm_medium=App.net&utm_source=PourOver)[J]. arXiv preprint arXiv:1404.2188, 2014.

06.understand_sentiment/index.html

@@ -61,7 +61,7 @@ In natural language processing, sentiment analysis can be categorized as a **Tex
 
 The BOW model does not capture all the information in a piece of text, as it ignores syntax and grammar and just treats the text as a set of words. For example, “this movie is extremely bad“ and “boring, dull, and empty work” describe very similar semantic meaning, yet their BOW representations have very little similarity. Furthermore, “the movie is bad“ and “the movie is not bad“ have high similarity with BOW features, but they express completely opposite semantics.
 
-This chapter introduces a deep learning model that handles these issues in BOW. Our model embeds texts into a low-dimensional space and takes word order into consideration. It is an end-to-end framework and it has large performance improvement over traditional methods \[[1](#Reference)\].
+This chapter introduces a deep learning model that handles these issues in BOW. Our model embeds texts into a low-dimensional space and takes word order into consideration. It is an end-to-end framework and it has large performance improvement over traditional methods \[[1](#references)\].
 
 ## Model Overview
 
@@ -74,11 +74,11 @@ The convolutional neural network for texts is introduced in chapter [recommender
 
 CNN mainly contains convolution and pooling operation, with versatile combinations in various applications. We firstly apply the convolution operation: we apply the kernel in each window, extracting features. Convolving by the kernel at every window produces a feature map. Next, we apply *max pooling* over time to represent the whole sentence, which is the maximum element across the feature map. In real applications, we will apply multiple CNN kernels on the sentences. It can be implemented efficiently by concatenating the kernels together as a matrix. Also, we can use CNN kernels with different kernel size. Finally, concatenating the resulting features produces a fixed-length representation, which can be combined with a softmax to form the model for the sentiment analysis problem.
 
-For short texts, the aforementioned CNN model can achieve very high accuracy \[[1](#Reference)\]. If we want to extract more abstract representations, we may apply a deeper CNN model \[[2](#Reference),[3](#Reference)\].
+For short texts, the aforementioned CNN model can achieve very high accuracy \[[1](#references)\]. If we want to extract more abstract representations, we may apply a deeper CNN model \[[2](#references),[3](#references)\].
 
 ### Recurrent Neural Network (RNN)
 
-RNN is an effective model for sequential data. In terms of computability, the RNN is Turing-complete \[[4](#Reference)\]. Since NLP is a classical problem of sequential data, the RNN, especially its variant LSTM\[[5](#Reference)\]), achieves state-of-the-art performance on various NLP tasks, such as language modeling, syntax parsing, POS-tagging, image captioning, dialog, machine translation, and so forth.
+RNN is an effective model for sequential data. In terms of computability, the RNN is Turing-complete \[[4](#references)\]. Since NLP is a classical problem of sequential data, the RNN, especially its variant LSTM\[[5](#references)\]), achieves state-of-the-art performance on various NLP tasks, such as language modeling, syntax parsing, POS-tagging, image captioning, dialog, machine translation, and so forth.
 
 <p align="center">
 <img src="image/rnn.png" width = "60%" align="center"/><br/>
@@ -95,13 +95,13 @@ In NLP, words are often represented as one-hot vectors and then mapped to an emb
 
 ### Long-Short Term Memory (LSTM)
 
-Training an RNN on long sequential data sometimes leads to the gradient vanishing or exploding\[[6](#)\]. To solve this problem Hochreiter S, Schmidhuber J. (1997) proposed **Long Short Term Memory** (LSTM)\[[5](#Reference)\]).
+Training an RNN on long sequential data sometimes leads to the gradient vanishing or exploding\[[6](#references)\]. To solve this problem Hochreiter S, Schmidhuber J. (1997) proposed **Long Short Term Memory** (LSTM)\[[5](#references)\]).
 
 Compared to the structure of a simple RNN, an LSTM includes memory cell $c$, input gate $i$, forget gate $f$ and output gate $o$. These gates and memory cells dramatically improve the ability for the network to handle long sequences. We can formulate the **LSTM-RNN**, denoted as a function $F$, as follows：
 
 $$ h_t=F(x_t,h_{t-1})$$
 
-$F$ contains following formulations\[[7](#Reference)\]：
+$F$ contains following formulations\[[7](#references)\]：
 \begin{align}
 i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
 f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
@@ -124,7 +124,7 @@ where $Recrurent$ is a simple RNN, GRU or LSTM.
 
 ### Stacked Bidirectional LSTM
 
-For vanilla LSTM, $h_t$ contains input information from previous time-step $1..t-1$ context. We can also apply an RNN with reverse-direction to take successive context $t+1…n$ into consideration. Combining constructing deep RNN (deeper RNN can contain more abstract and higher level semantic), we can design structures with deep stacked bidirectional LSTM to model sequential data\[[9](#Reference)\].
+For vanilla LSTM, $h_t$ contains input information from previous time-step $1..t-1$ context. We can also apply an RNN with reverse-direction to take successive context $t+1…n$ into consideration. Combining constructing deep RNN (deeper RNN can contain more abstract and higher level semantic), we can design structures with deep stacked bidirectional LSTM to model sequential data\[[9](#references)\].
 
 As shown in Figure 3 (3-layer RNN), odd/even layers are forward/reverse LSTM. Higher layers of LSTM take lower-layers LSTM as input, and the top-layer LSTM produces a fixed length vector by max-pooling (this representation considers contexts from previous and successive words for higher-level abstractions). Finally, we concatenate the output to a softmax layer for classification.
 
@@ -373,7 +373,7 @@ trainer.train(
 
 In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters, we will see how these models can be applied in other tasks.
 
-## Reference
+## References
 
 1. Kim Y. [Convolutional neural networks for sentence classification](http://arxiv.org/pdf/1408.5882)[J]. arXiv preprint arXiv:1408.5882, 2014.
 2. Kalchbrenner N, Grefenstette E, Blunsom P. [A convolutional neural network for modeling sentences](http://arxiv.org/pdf/1404.2188.pdf?utm_medium=App.net&utm_source=PourOver)[J]. arXiv preprint arXiv:1404.2188, 2014.

07.label_semantic_roles/README.md

@@ -31,7 +31,7 @@ Fig 1. Syntax tree
 </div>
 
 
-However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
+However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
 
 The BIO representation of above example is shown in Fig.1.
 
@@ -54,7 +54,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ### Stacked Recurrent Neural Network
 
-*Deep Neural Networks* can extract hierarchical representations. The higher layers can form relatively abstract/complex representations, based on primitive features discovered through the lower layers. Unfolding LSTMs through time results in a deep feed-forward neural network. This is because any computational path between the input at time $k < t$ to the output at time $t$ crosses several nonlinear layers. On the other hand, due to parameter sharing over time, LSTMs are also *shallow*; that is, the computation carried out at each time-step is just a linear transformation. Deep LSTM networks are typically constructed by stacking multiple LSTM layers on top of each other and taking the output from lower LSTM layer at time $t$ as the input of upper LSTM layer at time $t$. Deep, hierarchical neural networks can be efficient at representing some functions and modeling varying-length dependencies\[[2](#Reference)\].
+*Deep Neural Networks* can extract hierarchical representations. The higher layers can form relatively abstract/complex representations, based on primitive features discovered through the lower layers. Unfolding LSTMs through time results in a deep feed-forward neural network. This is because any computational path between the input at time $k < t$ to the output at time $t$ crosses several nonlinear layers. On the other hand, due to parameter sharing over time, LSTMs are also *shallow*; that is, the computation carried out at each time-step is just a linear transformation. Deep LSTM networks are typically constructed by stacking multiple LSTM layers on top of each other and taking the output from lower LSTM layer at time $t$ as the input of upper LSTM layer at time $t$. Deep, hierarchical neural networks can be efficient at representing some functions and modeling varying-length dependencies\[[2](#reference)\].
 
 
 However, in a deep LSTM network, any gradient propagated back in depth needs to traverse a large number of nonlinear steps. As a result, while LSTMs of 4 layers can be trained properly, those with 4-8 have much worse performance. Conventional LSTMs prevent back-propagated errors from vanishing or exploding by introducing shortcut connections to skip the intermediate nonlinear layers. Therefore, deep LSTMs can consider shortcut connections in depth as well.
@@ -70,7 +70,7 @@ Based on the stacked LSTMs, we add shortcut connections: take the input-to-hidde
 
 Fig.3 illustrates the final stacked recurrent neural networks.
 
-<p align="center">  
+<p align="center">
 <img src="./image/stacked_lstm_en.png" width = "40%"  align=center><br>
 Fig 3. Stacked Recurrent Neural Networks
 </p>
@@ -82,28 +82,28 @@ While LSTMs can summarize the history, they can not see the future. Because most
 To address this, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
 
 
-<p align="center">  
+<p align="center">
 <img src="./image/bidirectional_stacked_lstm_en.png" width = "60%" align=center><br>
 Fig 4. Bidirectional LSTMs
 </p>
 
-Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
+Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#reference), [4](#reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
 
 ### Conditional Random Field (CRF)
 
-Typically, a neural network's lower layers learn representations while its very top layer accomplishs the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
+Typically, a neural network's lower layers learn representations while its very top layer accomplishes the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
 
 
 The CRF is an undirected probabilistic graph with nodes denoting random variables and edges denoting dependencies between these variables. In essence, CRFs learn the conditional probability $P(Y|X)$, where $X = (x_1, x_2, ... , x_n)$ are sequences of input and $Y = (y_1, y_2, ... , y_n)$ are label sequences; to decode, simply search through $Y$ for a sequence that maximizes the conditional probability $P(Y|X)$, i.e., $Y^* = \mbox{arg max}_{Y} P(Y | X)$。
 
 Sequence tagging tasks do not assume a lot of conditional independence, because they only concern about the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
 
-<p align="center">  
+<p align="center">
 <img src="./image/linear_chain_crf.png" width = "35%" align=center><br>
 Fig 5. Linear Chain Conditional Random Field used in SRL tasks
 </p>
 
-By the fundamental theorem of random fields \[[5](#Reference)\], the joint distribution over the label sequence $Y$ given $X$ has the form:
+By the fundamental theorem of random fields \[[5](#reference)\], the joint distribution over the label sequence $Y$ given $X$ has the form:
 
 $$p(Y | X) = \frac{1}{Z(X)} \text{exp}\left(\sum_{i=1}^{n}\left(\sum_{j}\lambda_{j}t_{j} (y_{i - 1}, y_{i}, X, i) + \sum_{k} \mu_k s_k (y_i, X, i)\right)\right)$$
 
@@ -147,7 +147,7 @@ After these modifications, the model is as follows, as illustrated in Figure 6:
 4. Take the representation from step 3 as input to CRF, label sequence as a supervisory signal, and complete sequence tagging tasks.
 
 
-<div  align="center">  
+<div  align="center">
 <img src="image/db_lstm_network_en.png" width = "60%"  align=center /><br>
 Fig 6. DB-LSTM for SRL tasks
 </div>
@@ -165,7 +165,7 @@ conll05st-release/
     └── words  # text sequence
 ```
 
-The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](# references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
+The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](#references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
 
 The raw data needs to be preprocessed into formats that PaddlePaddle can handle. The preprocessing consists of the following steps:
 
@@ -266,7 +266,7 @@ Note that `hidden_dim = 512` means a LSTM hidden vector of 128 dimension (512/4)
 
 - Transform the word sequence itself, the predicate, the predicate context, and the region mark sequence into embedded vector sequences.
 
-```python  
+```python
 
 # Since word vectorlookup table is pre-trained, we won't update it this time.
 # is_static being True prevents updating the lookup table during training.
@@ -295,7 +295,7 @@ emb_layers.append(mark_embedding)
 
 - 8 LSTM units are trained through alternating left-to-right / right-to-left order denoted by the variable `reverse`.
 
-```python  
+```python
 hidden_0 = paddle.layer.mixed(
     size=hidden_dim,
     bias_attr=std_default,
@@ -522,7 +522,7 @@ print pre_lab
 
 Semantic Role Labeling is an important intermediate step in a wide range of natural language processing tasks. In this tutorial, we use SRL as an example to illustrate using PaddlePaddle to do sequence tagging tasks. The models proposed are from our published paper\[[10](#Reference)\]. We only use test data for illustration since the training data on the CoNLL 2005 dataset is not completely public. This aims to propose an end-to-end neural network model with fewer dependencies on natural language processing tools but is comparable, or even better than traditional models in terms of performance. Please check out our paper for more information and discussions.
 
-## Reference
+## References
 1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv:1406.1078, 2014.

07.label_semantic_roles/index.html

@@ -73,7 +73,7 @@ Fig 1. Syntax tree
 </div>
 
 
-However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
+However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
 
 The BIO representation of above example is shown in Fig.1.
 
@@ -96,7 +96,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ### Stacked Recurrent Neural Network
 
-*Deep Neural Networks* can extract hierarchical representations. The higher layers can form relatively abstract/complex representations, based on primitive features discovered through the lower layers. Unfolding LSTMs through time results in a deep feed-forward neural network. This is because any computational path between the input at time $k < t$ to the output at time $t$ crosses several nonlinear layers. On the other hand, due to parameter sharing over time, LSTMs are also *shallow*; that is, the computation carried out at each time-step is just a linear transformation. Deep LSTM networks are typically constructed by stacking multiple LSTM layers on top of each other and taking the output from lower LSTM layer at time $t$ as the input of upper LSTM layer at time $t$. Deep, hierarchical neural networks can be efficient at representing some functions and modeling varying-length dependencies\[[2](#Reference)\].
+*Deep Neural Networks* can extract hierarchical representations. The higher layers can form relatively abstract/complex representations, based on primitive features discovered through the lower layers. Unfolding LSTMs through time results in a deep feed-forward neural network. This is because any computational path between the input at time $k < t$ to the output at time $t$ crosses several nonlinear layers. On the other hand, due to parameter sharing over time, LSTMs are also *shallow*; that is, the computation carried out at each time-step is just a linear transformation. Deep LSTM networks are typically constructed by stacking multiple LSTM layers on top of each other and taking the output from lower LSTM layer at time $t$ as the input of upper LSTM layer at time $t$. Deep, hierarchical neural networks can be efficient at representing some functions and modeling varying-length dependencies\[[2](#reference)\].
 
 
 However, in a deep LSTM network, any gradient propagated back in depth needs to traverse a large number of nonlinear steps. As a result, while LSTMs of 4 layers can be trained properly, those with 4-8 have much worse performance. Conventional LSTMs prevent back-propagated errors from vanishing or exploding by introducing shortcut connections to skip the intermediate nonlinear layers. Therefore, deep LSTMs can consider shortcut connections in depth as well.
@@ -112,7 +112,7 @@ Based on the stacked LSTMs, we add shortcut connections: take the input-to-hidde
 
 Fig.3 illustrates the final stacked recurrent neural networks.
 
-<p align="center">  
+<p align="center">
 <img src="./image/stacked_lstm_en.png" width = "40%"  align=center><br>
 Fig 3. Stacked Recurrent Neural Networks
 </p>
@@ -124,28 +124,28 @@ While LSTMs can summarize the history, they can not see the future. Because most
 To address this, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
 
 
-<p align="center">  
+<p align="center">
 <img src="./image/bidirectional_stacked_lstm_en.png" width = "60%" align=center><br>
 Fig 4. Bidirectional LSTMs
 </p>
 
-Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
+Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#reference), [4](#reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
 
 ### Conditional Random Field (CRF)
 
-Typically, a neural network's lower layers learn representations while its very top layer accomplishs the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
+Typically, a neural network's lower layers learn representations while its very top layer accomplishes the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
 
 
 The CRF is an undirected probabilistic graph with nodes denoting random variables and edges denoting dependencies between these variables. In essence, CRFs learn the conditional probability $P(Y|X)$, where $X = (x_1, x_2, ... , x_n)$ are sequences of input and $Y = (y_1, y_2, ... , y_n)$ are label sequences; to decode, simply search through $Y$ for a sequence that maximizes the conditional probability $P(Y|X)$, i.e., $Y^* = \mbox{arg max}_{Y} P(Y | X)$。
 
 Sequence tagging tasks do not assume a lot of conditional independence, because they only concern about the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
 
-<p align="center">  
+<p align="center">
 <img src="./image/linear_chain_crf.png" width = "35%" align=center><br>
 Fig 5. Linear Chain Conditional Random Field used in SRL tasks
 </p>
 
-By the fundamental theorem of random fields \[[5](#Reference)\], the joint distribution over the label sequence $Y$ given $X$ has the form:
+By the fundamental theorem of random fields \[[5](#reference)\], the joint distribution over the label sequence $Y$ given $X$ has the form:
 
 $$p(Y | X) = \frac{1}{Z(X)} \text{exp}\left(\sum_{i=1}^{n}\left(\sum_{j}\lambda_{j}t_{j} (y_{i - 1}, y_{i}, X, i) + \sum_{k} \mu_k s_k (y_i, X, i)\right)\right)$$
 
@@ -189,7 +189,7 @@ After these modifications, the model is as follows, as illustrated in Figure 6:
 4. Take the representation from step 3 as input to CRF, label sequence as a supervisory signal, and complete sequence tagging tasks.
 
 
-<div  align="center">  
+<div  align="center">
 <img src="image/db_lstm_network_en.png" width = "60%"  align=center /><br>
 Fig 6. DB-LSTM for SRL tasks
 </div>
@@ -207,7 +207,7 @@ conll05st-release/
     └── words  # text sequence
 ```
 
-The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](# references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
+The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](#references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
 
 The raw data needs to be preprocessed into formats that PaddlePaddle can handle. The preprocessing consists of the following steps:
 
@@ -308,7 +308,7 @@ Note that `hidden_dim = 512` means a LSTM hidden vector of 128 dimension (512/4)
 
 - Transform the word sequence itself, the predicate, the predicate context, and the region mark sequence into embedded vector sequences.
 
-```python  
+```python
 
 # Since word vectorlookup table is pre-trained, we won't update it this time.
 # is_static being True prevents updating the lookup table during training.
@@ -337,7 +337,7 @@ emb_layers.append(mark_embedding)
 
 - 8 LSTM units are trained through alternating left-to-right / right-to-left order denoted by the variable `reverse`.
 
-```python  
+```python
 hidden_0 = paddle.layer.mixed(
     size=hidden_dim,
     bias_attr=std_default,
@@ -564,7 +564,7 @@ print pre_lab
 
 Semantic Role Labeling is an important intermediate step in a wide range of natural language processing tasks. In this tutorial, we use SRL as an example to illustrate using PaddlePaddle to do sequence tagging tasks. The models proposed are from our published paper\[[10](#Reference)\]. We only use test data for illustration since the training data on the CoNLL 2005 dataset is not completely public. This aims to propose an end-to-end neural network model with fewer dependencies on natural language processing tools but is comparable, or even better than traditional models in terms of performance. Please check out our paper for more information and discussions.
 
-## Reference
+## References
 1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv:1406.1078, 2014.

08.machine_translation/README.md

@@ -6,6 +6,7 @@ The source code of this tutorial is live at [book/machine_translation](https://g
 
 Machine translation (MT) leverages computers to translate from one language to another. The language to be translated is referred to as the source language, while the language to be translated into is referred to as the target language. Thus, Machine translation is the process of translating from the source language to the target language. It is one of the most important research topics in the field of natural language processing.
 
+
 Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one language. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#references)\].
 
 
@@ -57,7 +58,7 @@ This section will introduce Gated Recurrent Unit (GRU), Bi-directional Recurrent
 We already introduced RNN and LSTM in the [Sentiment Analysis](https://github.com/PaddlePaddle/book/blob/develop/understand_sentiment/README.md) chapter.
 Compared to a simple RNN, the LSTM added memory cell, input gate, forget gate and output gate. These gates combined with the memory cell greatly improve the ability to handle long-term dependencies.
 
-GRU\[[2](#References)\] proposed by Cho et al is a simplified LSTM and an extension of a simple RNN. It is shown in the figure below.
+GRU\[[2](#references)\] proposed by Cho et al is a simplified LSTM and an extension of a simple RNN. It is shown in the figure below.
 A GRU unit has only two gates:
 - reset gate: when this gate is closed, the history information is discarded, i.e., the irrelevant historical information has no effect on the future output.
 - update gate: it combines the input gate and the forget gate and is used to control the impact of historical information on the hidden output. The historical information is passed over when the update gate is close to 1.
@@ -68,11 +69,11 @@ Figure 2. A GRU Gate
 </p>
 
 Generally speaking, sequences with short distance dependencies will have an active reset gate while sequences with long distance dependency will have an active update date.
-In addition, Chung et al.\[[3](#References)\] have empirically shown that although GRU has less parameters, it has similar performance to LSTM on several different tasks.
+In addition, Chung et al.\[[3](#references)\] have empirically shown that although GRU has less parameters, it has similar performance to LSTM on several different tasks.
 
 ### Bi-directional Recurrent Neural Network
 
-We already introduced an instance of bi-directional RNN in the [Semantic Role Labeling](https://github.com/PaddlePaddle/book/blob/develop/label_semantic_roles/README.md) chapter. Here we present another bi-directional RNN model with a different architecture proposed by Bengio et al. in \[[2](#References),[4](#References)\]. This model takes a sequence as input and outputs a fixed dimensional feature vector at each step, encoding the context information at the corresponding time step.
+We already introduced an instance of bi-directional RNN in the [Semantic Role Labeling](https://github.com/PaddlePaddle/book/blob/develop/label_semantic_roles/README.md) chapter. Here we present another bi-directional RNN model with a different architecture proposed by Bengio et al. in \[[2](#references),[4](#references)\]. This model takes a sequence as input and outputs a fixed dimensional feature vector at each step, encoding the context information at the corresponding time step.
 
 Specifically, this bi-directional RNN processes the input sequence in the original and reverse order respectively, and then concatenates the output feature vectors at each time step as the final output. Thus the output node at each time step contains information from the past and future as context. The figure below shows an unrolled bi-directional RNN. This network contains a forward RNN and backward RNN with six weight matrices: weight matrices from input to forward hidden layer and backward hidden ($W_1, W_3$), weight matrices from hidden to itself ($W_2, W_5$), matrices from forward hidden and backward hidden to output layer ($W_4, W_6$). Note that there are no connections between forward hidden and backward hidden layers.
 
@@ -83,7 +84,7 @@ Figure 3. Temporally unrolled bi-directional RNN
 
 ### Encoder-Decoder Framework
 
-The Encoder-Decoder\[[2](#References)\] framework aims to solve the mapping of a sequence to another sequence, for sequences with arbitrary lengths. The source sequence is encoded into a vector via an encoder, which is then decoded to a target sequence via a decoder by maximizing the predictive probability. Both the encoder and the decoder are typically implemented via RNN.
+The Encoder-Decoder\[[2](#references)\] framework aims to solve the mapping of a sequence to another sequence, for sequences with arbitrary lengths. The source sequence is encoded into a vector via an encoder, which is then decoded to a target sequence via a decoder by maximizing the predictive probability. Both the encoder and the decoder are typically implemented via RNN.
 
 <p align="center">
 <img src="image/encoder_decoder_en.png" width=700><br/>
@@ -144,7 +145,7 @@ The generation process of machine translation is to translate the source sentenc
 
 There are a few problems with the fixed dimensional vector representation from the encoding stage:
   * It is very challenging to encode both the semantic and syntactic information a sentence with a fixed dimensional vector regardless of the length of the sentence.
-  * Intuitively, when translating a sentence, we typically pay more attention to the parts in the source sentence more relevant to the current translation. Moreover, the focus changes along the process of the translation. With a fixed dimensional vector, all the information from the source sentence is treated equally in terms of attention. This is not reasonable. Therefore, Bahdanau et al. \[[4](#References)\] introduced attention mechanism, which can decode based on different fragments of the context sequence in order to address the difficulty of feature learning for long sentences. Decoder with attention will be explained in the following.
+  * Intuitively, when translating a sentence, we typically pay more attention to the parts in the source sentence more relevant to the current translation. Moreover, the focus changes along the process of the translation. With a fixed dimensional vector, all the information from the source sentence is treated equally in terms of attention. This is not reasonable. Therefore, Bahdanau et al. \[[4](#references)\] introduced attention mechanism, which can decode based on different fragments of the context sequence in order to address the difficulty of feature learning for long sentences. Decoder with attention will be explained in the following.
 
 Different from the simple decoder, $z_i$ is computed as:
 
@@ -400,7 +401,7 @@ is_generating = False
            max_length=max_length)
    ```
 
-Note: Our configuration is based on Bahdanau et al. \[[4](#Reference)\] but with a few simplifications. Please refer to [issue #1133](https://github.com/PaddlePaddle/Paddle/issues/1133) for more details.
+Note: Our configuration is based on Bahdanau et al. \[[4](#references)\] but with a few simplifications. Please refer to [issue #1133](https://github.com/PaddlePaddle/Paddle/issues/1133) for more details.
 
 ## Model Training
 

08.machine_translation/index.html

@@ -48,6 +48,7 @@ The source code of this tutorial is live at [book/machine_translation](https://g
 
 Machine translation (MT) leverages computers to translate from one language to another. The language to be translated is referred to as the source language, while the language to be translated into is referred to as the target language. Thus, Machine translation is the process of translating from the source language to the target language. It is one of the most important research topics in the field of natural language processing.
 
+
 Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one language. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#references)\].
 
 
@@ -99,7 +100,7 @@ This section will introduce Gated Recurrent Unit (GRU), Bi-directional Recurrent
 We already introduced RNN and LSTM in the [Sentiment Analysis](https://github.com/PaddlePaddle/book/blob/develop/understand_sentiment/README.md) chapter.
 Compared to a simple RNN, the LSTM added memory cell, input gate, forget gate and output gate. These gates combined with the memory cell greatly improve the ability to handle long-term dependencies.
 
-GRU\[[2](#References)\] proposed by Cho et al is a simplified LSTM and an extension of a simple RNN. It is shown in the figure below.
+GRU\[[2](#references)\] proposed by Cho et al is a simplified LSTM and an extension of a simple RNN. It is shown in the figure below.
 A GRU unit has only two gates:
 - reset gate: when this gate is closed, the history information is discarded, i.e., the irrelevant historical information has no effect on the future output.
 - update gate: it combines the input gate and the forget gate and is used to control the impact of historical information on the hidden output. The historical information is passed over when the update gate is close to 1.
@@ -110,11 +111,11 @@ Figure 2. A GRU Gate
 </p>
 
 Generally speaking, sequences with short distance dependencies will have an active reset gate while sequences with long distance dependency will have an active update date.
-In addition, Chung et al.\[[3](#References)\] have empirically shown that although GRU has less parameters, it has similar performance to LSTM on several different tasks.
+In addition, Chung et al.\[[3](#references)\] have empirically shown that although GRU has less parameters, it has similar performance to LSTM on several different tasks.
 
 ### Bi-directional Recurrent Neural Network
 
-We already introduced an instance of bi-directional RNN in the [Semantic Role Labeling](https://github.com/PaddlePaddle/book/blob/develop/label_semantic_roles/README.md) chapter. Here we present another bi-directional RNN model with a different architecture proposed by Bengio et al. in \[[2](#References),[4](#References)\]. This model takes a sequence as input and outputs a fixed dimensional feature vector at each step, encoding the context information at the corresponding time step.
+We already introduced an instance of bi-directional RNN in the [Semantic Role Labeling](https://github.com/PaddlePaddle/book/blob/develop/label_semantic_roles/README.md) chapter. Here we present another bi-directional RNN model with a different architecture proposed by Bengio et al. in \[[2](#references),[4](#references)\]. This model takes a sequence as input and outputs a fixed dimensional feature vector at each step, encoding the context information at the corresponding time step.
 
 Specifically, this bi-directional RNN processes the input sequence in the original and reverse order respectively, and then concatenates the output feature vectors at each time step as the final output. Thus the output node at each time step contains information from the past and future as context. The figure below shows an unrolled bi-directional RNN. This network contains a forward RNN and backward RNN with six weight matrices: weight matrices from input to forward hidden layer and backward hidden ($W_1, W_3$), weight matrices from hidden to itself ($W_2, W_5$), matrices from forward hidden and backward hidden to output layer ($W_4, W_6$). Note that there are no connections between forward hidden and backward hidden layers.
 
@@ -125,7 +126,7 @@ Figure 3. Temporally unrolled bi-directional RNN
 
 ### Encoder-Decoder Framework
 
-The Encoder-Decoder\[[2](#References)\] framework aims to solve the mapping of a sequence to another sequence, for sequences with arbitrary lengths. The source sequence is encoded into a vector via an encoder, which is then decoded to a target sequence via a decoder by maximizing the predictive probability. Both the encoder and the decoder are typically implemented via RNN.
+The Encoder-Decoder\[[2](#references)\] framework aims to solve the mapping of a sequence to another sequence, for sequences with arbitrary lengths. The source sequence is encoded into a vector via an encoder, which is then decoded to a target sequence via a decoder by maximizing the predictive probability. Both the encoder and the decoder are typically implemented via RNN.
 
 <p align="center">
 <img src="image/encoder_decoder_en.png" width=700><br/>
@@ -186,7 +187,7 @@ The generation process of machine translation is to translate the source sentenc
 
 There are a few problems with the fixed dimensional vector representation from the encoding stage:
   * It is very challenging to encode both the semantic and syntactic information a sentence with a fixed dimensional vector regardless of the length of the sentence.
-  * Intuitively, when translating a sentence, we typically pay more attention to the parts in the source sentence more relevant to the current translation. Moreover, the focus changes along the process of the translation. With a fixed dimensional vector, all the information from the source sentence is treated equally in terms of attention. This is not reasonable. Therefore, Bahdanau et al. \[[4](#References)\] introduced attention mechanism, which can decode based on different fragments of the context sequence in order to address the difficulty of feature learning for long sentences. Decoder with attention will be explained in the following.
+  * Intuitively, when translating a sentence, we typically pay more attention to the parts in the source sentence more relevant to the current translation. Moreover, the focus changes along the process of the translation. With a fixed dimensional vector, all the information from the source sentence is treated equally in terms of attention. This is not reasonable. Therefore, Bahdanau et al. \[[4](#references)\] introduced attention mechanism, which can decode based on different fragments of the context sequence in order to address the difficulty of feature learning for long sentences. Decoder with attention will be explained in the following.
 
 Different from the simple decoder, $z_i$ is computed as:
 
@@ -442,7 +443,7 @@ is_generating = False
            max_length=max_length)
    ```
 
-Note: Our configuration is based on Bahdanau et al. \[[4](#Reference)\] but with a few simplifications. Please refer to [issue #1133](https://github.com/PaddlePaddle/Paddle/issues/1133) for more details.
+Note: Our configuration is based on Bahdanau et al. \[[4](#references)\] but with a few simplifications. Please refer to [issue #1133](https://github.com/PaddlePaddle/Paddle/issues/1133) for more details.
 
 ## Model Training