427,866 research outputs found
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
Solving General Arithmetic Word Problems
This paper presents a novel approach to automatically solving arithmetic word
problems. This is the first algorithmic approach that can handle arithmetic
problems with multiple steps and operations, without depending on additional
annotations or predefined templates. We develop a theory for expression trees
that can be used to represent and evaluate the target arithmetic expressions;
we use it to uniquely decompose the target arithmetic problem to multiple
classification problems; we then compose an expression tree, combining these
with world knowledge through a constrained inference framework. Our classifiers
gain from the use of {\em quantity schemas} that supports better extraction of
features. Experimental results show that our method outperforms existing
systems, achieving state of the art performance on benchmark datasets of
arithmetic word problems.Comment: EMNLP 201
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