The heart of Aspect-based Sentiment Analysis (ABSA) task is how to connect aspects with their respective opinion words effectively. In order to get their connection, we can compute their coefficient.
In this article, we introduce a method to compute the weight between words when building a word graph. It is very useful.
In this article, we will introduce how to create a word graph from a document or dataset. This word graph can be used in some Graph Neural Networks (GNN).
In this article, we will introduce three methods to incorporate external knowledge in recurrent neural networks (RNNs) for text classification.
In this article, we will introduce how to use negative supervision method in multitask text classification.
Attention mechanism is widely used in deep learning. It has been proved that it can improve the performance of deep learning model.
This tutorial will discuss how to use bert model for multi-task learning. You can build your custom model from this post.
Layer Normalization can prevent model over-fitting and speed up the model training. In this tutorial, we will introduce this topic.
There are a lot of linguistic knowledge and sentiment resources nowadays. For example: sentiment lexicons. We can add these resources to improve sentiment classification.
Embedding attention is proposed in paper: Lexicon Integrated CNN Models with Attention for Sentiment Analysis. In this note, we will introduce it.