Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A New Recurrent Neural CRF for Learning Non-linear Edge Features   Conditional Random Field (CRF) and recurrent neural models have achieved
success in structured prediction. More recently, there is a marriage of CRF and
recurrent neural models, so that we can gain from both non-linear dense
features and globally normalized CRF objective. These recurrent neural CRF
models mainly focus on encode node features in CRF undirected graphs. However,
edge features prove important to CRF in structured prediction. In this work, we
introduce a new recurrent neural CRF model, which learns non-linear edge
features, and thus makes non-linear features encoded completely. We compare our
model with different neural models in well-known structured prediction tasks.
Experiments show that our model outperforms state-of-the-art methods in NP
chunking, shallow parsing, Chinese word segmentation and POS tagging.

26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics