Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inducing Distant Supervision in Suggestion Mining through Part-of-Speech
  Embeddings   Mining suggestion expressing sentences from a given text is a less
investigated sentence classification task, and therefore lacks hand labeled
benchmark datasets. In this work, we propose and evaluate two approaches for
distant supervision in suggestion mining. The distant supervision is obtained
through a large silver standard dataset, constructed using the text from
wikiHow and Wikipedia. Both the approaches use a LSTM based neural network
architecture to learn a classification model for suggestion mining, but vary in
their method to use the silver standard dataset. The first approach directly
trains the classifier using this dataset, while the second approach only learns
word embeddings from this dataset. In the second approach, we also learn POS
embeddings, which interestingly gives the best classification accuracy.

31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph