Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: pioNER: Datasets and Baselines for Armenian Named Entity Recognition   In this work, we tackle the problem of Armenian named entity recognition,
providing silver- and gold-standard datasets as well as establishing baseline
results on popular models. We present a 163000-token named entity corpus
automatically generated and annotated from Wikipedia, and another 53400-token
corpus of news sentences with manual annotation of people, organization and
location named entities. The corpora were used to train and evaluate several
popular named entity recognition models. Alongside the datasets, we release
50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection
of Armenian texts from Wikipedia, news, blogs, and encyclopedia.

37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics