Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human
  Needs   To make machines better understand sentiments, research needs to move from
polarity identification to understanding the reasons that underlie the
expression of sentiment. Categorizing the goals or needs of humans is one way
to explain the expression of sentiment in text. Humans are good at
understanding situations described in natural language and can easily connect
them to the character's psychological needs using commonsense knowledge. We
present a novel method to extract, rank, filter and select multi-hop relation
paths from a commonsense knowledge resource to interpret the expression of
sentiment in terms of their underlying human needs. We efficiently integrate
the acquired knowledge paths in a neural model that interfaces context
representations with knowledge using a gated attention mechanism. We assess the
model's performance on a recently published dataset for categorizing human
needs. Selectively integrating knowledge paths boosts performance and
establishes a new state-of-the-art. Our model offers interpretability through
the learned attention map over commonsense knowledge paths. Human evaluation
highlights the relevance of the encoded knowledge.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys