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Text Classification for High School Exam Questions

Highlights:

  1. This is a multi-class text classification (document classification) problem.
  2. The purpose of this project is to classify High School Exam Questions into some classes and the number of classes is related to the data set.

demands:

  1. You can solve this problem with a variety of machine learning algorithms.
  2. The evaluation method is mainly based on precision and recall.

Data:

Chinese exam questions of high school.

Example:

  • 1354263077 　 21 　 字音　 下列词语中加点的字,读音全都正确的一组是( ) A.尴 尬(ɡà) 口 讷(nà) 髭须(xī) 朔风(shuò) B.拾 掇(duo) 央 浼(měi) 规 矩(jù) 祈祷(qí) C.妥 当(dànɡ) 憎恶(zēnɡ) 滑 稽(jī) 吼 啸(xiào) D. 赍发(jī) 盘 缠(chan) 玷辱(diàn) 胭 脂(zhǐ)
  • 1354307841 　 21 　 古诗词阅读　 阅读下面这首词,然后回答问题. 望江怨 送别 [清]万树 春江渺,断送扁舟过林杪①.愁云清未了,布帆遥比沙鸥小.恨残照,犹有一竿红.怪人催去早. [注]①杪:树梢. (1)这首词的前四句描写了怎样的送别场景? (2)怎样理解“怪人催去早“?请结合全词分析.

       * 1354263077 is ID of the question.
       * 21 is the ID of the dataset, so you can ignore it if you don't want to try different datasets.
       * 字音 is one of the classes.

Train-test split:

In order to unify the standard, we use the questions whose ID end with 9 as the test set and the rest as the train set.

Evaluation:

def count_precision_recall_at_k(y_pred, y_true, k):
    """
    y_pred: [[ 1.3315865   0.71527897 -1.54540029 -0.00838385  0.62133597 -0.72008556]]
    y_true: [[0 0 1 1 0 0]
    """
    y_indices = y_pred.argsort()[:, -k:][:, ::-1]
    pre = 0.0
    rec = 0.0
    for i in range(len(y_true)):
        intersec_true = 0
        for j in y_indices[i]:
            intersec_true += y_true[i][j]
        true_total_count = np.count_nonzero(y_true[i] == 1)
        pred_total_count = len(y_indices[i])
        pre += intersec_true*1.0/pred_total_count
        rec += intersec_true*1.0/true_total_count
    return pre/len(y_true), rec/len(y_true)

Baseline:

baseline pre_1 rec_1 pre_2 rec_2 pre_3 rec_3
baseline_1 81.05 76.59 48.63 88.69 33.77 92.35
baseline_2 85.27 80.84 49.31 90.40 33.73 92.67

These baselines are the results of two different algorithms.

Reference:

Character-level Convolutional Networks for Text Classification
Convolutional Neural Networks for Sentence Classification
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
Very Deep Convolutional Networks for Text Classification
Hierarchical Attention Networks for Document Classification