Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast(er) Exact Decoding and Global Training for Transition-Based
  Dependency Parsing via a Minimal Feature Set   We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Kuhlmann et al. (2011) to produce the first implementation of
worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition
systems. With our minimal features, we also present O(n^3) global training
methods. Finally, using ensembles including our new parsers, we achieve the
best unlabeled attachment score reported (to our knowledge) on the Chinese
Treebank and the "second-best-in-class" result on the English Penn Treebank.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex