Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Context-Aware Representations of Subtrees   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.

6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th