Learning Context-Aware Representations of Subtrees
This addresses web page and element classification, offering incremental improvements for applications like reinforcement learning over the Web.
The paper tackled the problem of classifying web elements as subtrees in DOM trees by incorporating context, achieving an average F1-score of 0.7973 on a multi-class web classification task.
This thesis tackles the problem of learning efficient representations of complex, structured data with a natural application to web page and element classification. We hypothesise that the context around the element inside the web page is of high value to the problem and is currently under exploited. This thesis aims to solve the problem of classifying web elements as subtrees of a DOM tree by also considering their context. To achieve this, first we discuss current expert knowledge systems that work on structures, such as Tree-LSTM. Then, we propose context-aware extensions to this model. We show that the new model achieves an average F1-score of 0.7973 on a multi-class web classification task. This model generates better representations for various subtrees and may be used for applications such element classification, state estimators in reinforcement learning over the Web and more.