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Text classification models (including traditional machine learning methods and deep learning methods)

License: MIT License

Python 100.00%

text-classification natural-language-processing machine-learning deep-learning

text_classification's Introduction

Text Classification

The goal of this repository is to implement text classification in traditional machine learning methods and deep learning methods (in Pytorch).

Note: Since I have to spend more time in conducting research, I stop reproducing classic papers on the task of text classification. However, I will still maintain this project. Any contributions lke issues and pull requests are welcome.

Models

Feature Extraction

BOW (Bag of Words)
TFIDF (Term Frequency-Inverse Document Frequency)
N-gram

Traditional Machine Learning Methods

Deep Learning Methods

DAN: Deep Unordered Composition Rivals Syntactic Methods for Text Classification
TextCNN: Convolutional Neural Networks for Sentence Classification
TextRNN: Recurrent Neural Network for Text Classification with Multi-Task Learning
~~RCNN~~
~~Capsule Network~~
~~Transformer~~
~~Elmo~~
~~BERT~~

Experiments

Effects of Feature Extraction Methods

Methods	KNN	Decision Tree	Perceptron	Bagging	Random Forest	AdaBoost	Gradient Boosting	Naive Bayes	SVM	Logistic Regression
BoW (ngram-range=(1, 1))	0.665	0.707	0.742	0.749	0.757	0.724	0.713	0.800	0.823	0.826
BoW (ngram-range=(1, 2))	0.666	0.699	0.750	0.744	0.751	0.724	0.712	0.795	0.819	0.823
BoW (ngram-range=(1, 3))	0.667	0.700	0.712	0.748	0.757	0.724	0.712	0.795	0.818	0.824
BoW (ngram-range=(2, 2))	0.579	0.628	0.652	0.646	0.652	0.584	0.600	0.669	0.671	0.692
BoW (ngram-range=(2, 3))	0.578	0.625	0.625	0.636	0.648	0.584	0.600	0.662	0.667	0.684
BoW (ngram-range=(3, 3))	0.536	0.572	0.525	0.578	0.581	0.532	0.539	0.561	0.576	0.590
TFIDF	0.714	0.705	0.594	0.759	0.760	0.723	0.714	0.807	0.804	0.824