Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Sentiment Annotations to Sentiment Prediction through Discourse
  Augmentation   Sentiment analysis, especially for long documents, plausibly requires methods
capturing complex linguistics structures. To accommodate this, we propose a
novel framework to exploit task-related discourse for the task of sentiment
analysis. More specifically, we are combining the large-scale,
sentiment-dependent MEGA-DT treebank with a novel neural architecture for
sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.
Experiments show that our framework using sentiment-related discourse
augmentations for sentiment prediction enhances the overall performance for
long documents, even beyond previous approaches using well-established
discourse parsers trained on human annotated data. We show that a simple
ensemble approach can further enhance performance by selectively using
discourse, depending on the document length.

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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
3
The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex