Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ner and pos when nothing is capitalized   For those languages which use it, capitalization is an important signal for
the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech
(POS) tagging. In fact, it is such a strong signal that model performance on
these tasks drops sharply in common lowercased scenarios, such as noisy web
text or machine translation outputs. In this work, we perform a systematic
analysis of solutions to this problem, modifying only the casing of the train
or test data using lowercasing and truecasing methods. While prior work and
first impressions might suggest training a caseless model, or using a truecaser
at test time, we show that the most effective strategy is a concatenation of
cased and lowercased training data, producing a single model with high
performance on both cased and uncased text. As shown in our experiments, this
result holds across tasks and input representations. Finally, we show that our
proposed solution gives an 8% F1 improvement in mention detection on noisy
out-of-domain Twitter data.

19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th