Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-task Learning of Negation and Speculation for Targeted Sentiment
  Classification   The majority of work in targeted sentiment analysis has concentrated on
finding better methods to improve the overall results. Within this paper we
show that these models are not robust to linguistic phenomena, specifically
negation and speculation. In this paper, we propose a multi-task learning
method to incorporate information from syntactic and semantic auxiliary tasks,
including negation and speculation scope detection, to create English-language
models that are more robust to these phenomena. Further we create two challenge
datasets to evaluate model performance on negated and speculative samples. We
find that multi-task models and transfer learning via language modelling can
improve performance on these challenge datasets, but the overall performances
indicate that there is still much room for improvement. We release both the
datasets and the source code at
https://github.com/jerbarnes/multitask_negation_for_targeted_sentiment.

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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg