Text Classification Multi-Label: An In-Depth Introduction
Introduction
In the field of text classification, two primary tasks emerge: multi-class classification and multi-label classification. Multi-class classification involves assigning a single category to an input data from multiple possible categories. For instance, determining a person's gender falls under multi-class classification, where the result can be either "male" or "female." Similarly, assessing the sentiment of a text as "positive," "neutral," or "negative" is another example.
In contrast, multi-label classification allows for multiple categories to be applied to a single dataset entry. A news article, for instance, could simultaneously be tagged as both "entertainment" and "sports," or it may belong solely to the "entertainment" category or any other.
Algorithms
The project presents four distinct methodologies for conducting multi-label text classification:
1. classifier_multi_label
- Utilizes the BERT model's first token ([CLS]) vector of shape (batch_size, hidden_size).
- Employs the
tf.nn.sigmoid_cross_entropy_with_logitsloss function. - Uses the
tf.wherefunction to select IDs with probabilities lower than 0.5.
2. classifier_multi_label_textcnn
- Uses BERT's output represented as a three-dimensional vector of shape (batch_size, sequence_length, hidden_size), which is then fed into a TextCNN layer.
- Employs the
tf.nn.sigmoid_cross_entropy_with_logitsloss function. - Uses the
tf.wherefunction to select IDs with probabilities lower than 0.5.
3. classifier_multi_label_denses
- Takes BERT's first token ([CLS]) vector of shape (batch_size, hidden_size) and passes it through multiple binary classifiers (fully connected layers) to solve the multi-label task.
- Uses the
tf.nn.softmax_cross_entropy_with_logitsloss function. - Utilizes the
tf.argmaxfunction for selecting the highest probability output.
4. classifier_multi_label_seq2seq
- Utilizes BERT's output as a three-dimensional vector of shape (batch_size, sequence_length, hidden_size), which is processed through an seq2seq+attention layer.
- Employs the
tf.nn.softmax_cross_entropy_with_logitsloss function. - Uses beam search for decoding the output probabilities.
Experiments
Training Process
The project demonstrates a comprehensive training process to optimize model performance.
Experiment Results
The experiments yield insightful results, showcasing the efficacy of different models under various parameters and conditions.
Conclusions
- For applications where inference speed is not critical, the ALBERT+Seq2Seq_Attention framework yields the best multi-label classification results.
- For scenarios where both speed and model efficiency are crucial, the ALBERT+TextCNN framework emerges as an optimal choice.
References
The project builds on extensive research and previous work detailed in several articles focusing on multi-label text classification using ALBERT and various model structures. These references provide deeper insights and comparisons across different approaches.
- Introduction and Comparison of Multi-Label Text Classification
- Multi-Label Text Classification [ALBERT]
- Multi-Label Text Classification [ALBERT+TextCNN]
- Multi-Label Text Classification [ALBERT+Multi_Denses]
- Multi-Label Text Classification [ALBERT+Seq2Seq+Attention]
This comprehensive overview captures the essence of the project, highlighting its methodologies, experiments, results, and conclusions, setting a foundation for further exploration and application in multi-label text classification.