7,792 research outputs found
Recursive Neural Networks Can Learn Logical Semantics
Tree-structured recursive neural networks (TreeRNNs) for sentence meaning
have been successful for many applications, but it remains an open question
whether the fixed-length representations that they learn can support tasks as
demanding as logical deduction. We pursue this question by evaluating whether
two such models---plain TreeRNNs and tree-structured neural tensor networks
(TreeRNTNs)---can correctly learn to identify logical relationships such as
entailment and contradiction using these representations. In our first set of
experiments, we generate artificial data from a logical grammar and use it to
evaluate the models' ability to learn to handle basic relational reasoning,
recursive structures, and quantification. We then evaluate the models on the
more natural SICK challenge data. Both models perform competitively on the SICK
data and generalize well in all three experiments on simulated data, suggesting
that they can learn suitable representations for logical inference in natural
language
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Intelligent systems based on first-order logic on the one hand, and on
artificial neural networks (also called connectionist systems) on the other,
differ substantially. It would be very desirable to combine the robust neural
networking machinery with symbolic knowledge representation and reasoning
paradigms like logic programming in such a way that the strengths of either
paradigm will be retained. Current state-of-the-art research, however, fails by
far to achieve this ultimate goal. As one of the main obstacles to be overcome
we perceive the question how symbolic knowledge can be encoded by means of
connectionist systems: Satisfactory answers to this will naturally lead the way
to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
"Not not bad" is not "bad": A distributional account of negation
With the increasing empirical success of distributional models of
compositional semantics, it is timely to consider the types of textual logic
that such models are capable of capturing. In this paper, we address
shortcomings in the ability of current models to capture logical operations
such as negation. As a solution we propose a tripartite formulation for a
continuous vector space representation of semantics and subsequently use this
representation to develop a formal compositional notion of negation within such
models.Comment: 9 pages, to appear in Proceedings of the 2013 Workshop on Continuous
Vector Space Models and their Compositionalit
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