Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Large-Scale Exploration of Effective Global Features for a Joint
  Entity Detection and Tracking Model   Entity detection and tracking (EDT) is the task of identifying textual
mentions of real-world entities in documents, extending the named entity
detection and coreference resolution task by considering mentions other than
names (pronouns, definite descriptions, etc.). Like NE tagging and coreference
resolution, most solutions to the EDT task separate out the mention detection
aspect from the coreference aspect. By doing so, these solutions are limited to
using only local features for learning. In contrast, by modeling both aspects
of the EDT task simultaneously, we are able to learn using highly complex,
non-local features. We develop a new joint EDT model and explore the utility of
many features, demonstrating their effectiveness on this task.

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The categories of this paper are: funct-an
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math