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After searching from Google, here is easiest solution. This should also works on other languages:

import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import matplotlib.font_manager as fm
f = "/System/Library/Fonts/PingFang.ttc"
prop = fm.FontProperties(fname=f)

plt.title("你好",fontproperties=prop)
plt.show()

Output:

LSTM #

The avoid the problem of vanishing gradient and exploding gradient in vanilla RNN, LSTM was published, which can remember information for longer periods of time.

Here is the structure of LSTM:

Generally, word2vec is a language model to predict the words probability based on the context. When build the model, it create word embedding for each word, and word embedding is widely used in many NLP tasks.

Models #

CBOW (Continuous Bag of Words) #

Use the context to predict the probability of current word. (In the picture, the word is encoded with one-hot encoding, \(W_{V*N}\) is word embedding, and \(W_{V*N}^{’}\), the output weight matrix in hidden layer, is same as \(\hat{\upsilon}\) in following equations)

Here is a simple way to classify text without much human effort and get a impressive performance.

It can be divided into two steps:

1. Get train data by using keyword classification
2. Generate a more accurate classification model by using doc2vec and label spreading

Keyword-based Classification #

Keyword based classification is a simple but effective method. Extracting the target keyword is a monotonous work. I use this method to automatic extract keyword candidate.

Here are some parameter in gensim’s doc2vec class.

window #

window is the maximum distance between the predicted word and context words used for prediction within a document. It will look behind and ahead.

As I said before, I’m working on a text classification project. I use doc2vec to convert text into vectors, then I use LPA to classify the vectors.

LPA is a simple, effective semi-supervised algorithm. It can use the density of unlabeled data to find a hyperplane to split the data.

These days, I’m working on some text classification works, and I use gensim ’s doc2vec function.

When using gensim, it shows this warning message:

C extension not loaded for Word2Vec, training will be slow.

I search this on Internet and found that gensim has rewrite some part of the code using cython rather than numpy to get better performance. A compiler is required to enable this feature.

Here are some shell tools I use, which can boost your productivity. Mordern-unix is a great repo that list lots of modern unix tools.

Prezto #

A zsh configuration framework. Provides auto completion, prompt theme and lots of modules to work with other useful tools. I extremely love the agnoster theme.

Over the years, I have read so many programmers’ blogs, which has helped me a lot. Now I think it’s the time to start my own blog.

I hope this can enforce myself to review what I have learned, and it would even be better if someone can benefit from it.