Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linguistically Regularized LSTMs for Sentiment Classification   Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification. Though a variety
of neural network models have been proposed very recently, however, previous
models either depend on expensive phrase-level annotation, whose performance
drops substantially when trained with only sentence-level annotation; or do not
fully employ linguistic resources (e.g., sentiment lexicons, negation words,
intensity words), thus not being able to produce linguistically coherent
representations. In this paper, we propose simple models trained with
sentence-level annotation, but also attempt to generating linguistically
coherent representations by employing regularizers that model the linguistic
role of sentiment lexicons, negation words, and intensity words. Results show
that our models are effective to capture the sentiment shifting effect of
sentiment, negation, and intensity words, while still obtain competitive
results without sacrificing the models' simplicity.

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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat