text classification by some machine learning algorithm.
| model | accuracy |
|---|---|
| SVC with Linear kernel | 0.718 |
$git clone [email protected]:gitabtion/text-classification.git
$cd text-classification
$python3 test.py
├── LICENSE
├── README.md
├── data
│ ├── stopwords.txt # stopword
│ ├── test_set.txt # testing set
│ ├── test_set_name.txt
│ ├── train_set.txt # trainning set
│ └── ver_set.txt # verification set
├── models
│ ├── __init__.py
│ └── svm.py # svm model
├── test.py
└── utils
├── __init__.py
├── data_helper.py # preprocess util of primer data which like test_set.txt upon
└── extract_samples.py # extracting samples from ACE data
- extract sentences for ace chinese data set.
- mark up the sentences in following types:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| not any class | life | movement | transaction | business | conflict | contact | personnel | justice |
if you using chinese data set, you have to using data_helper.py like:
train_text, train_labels, ver_text, ver_labels, test_text, test_labels = data_helper.get_data_and_labels()stopwords = data_helper.get_stopwords()# svm
model = SVM(train_text, train_labels, ver_text, ver_labels, test_text, test_labels, stopwords)model.train()
model.verification()
model.test()print('verification accuracy: {:.3}'.format(model.ver_acc))
print('test accuracy: {:.3}'.format(model.test_acc))