Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dependency-based Convolutional Neural Networks for Sentence Embedding   In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph