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The various benchmarks are a great opportunity to demonstrate the superiority of the symbolic approach over the neural network based models. I encourage you to
by paulsutter 10y ago
The various benchmarks are a great opportunity to demonstrate the superiority of the symbolic approach over the neural network based models. I encourage you to try, there's no better way to dispel the doubters.
- DonaldFisk 10y agoI don't think they're suitable for the same tasks, so I don't consider the two approaches to be in competition with each other. It's also very difficult if you're one person with limited time up against companies the size of Google. I'm busy on other things at the moment, but I intend to develop a rule-based system some time soon. I can rule out a neural network straight away because there's no data available, the rules are explicit, well documented, and have to be followed, and the system has to justify its reasoning. That is not to say I wouldn't consider using a neural network for a perception task.
- nitrogen 10y agoIs anybody working on using a neural network to build and update a symbolic graph, and vice versa? Or at least using a symbolic graph as an input to a NLP neural network, so the network could learn to rely on the symbolic graph when it is useful?
- rspeer 10y agoYes. ConceptNet [1] and distributional word embeddings go really well together, and can compare word meanings better than either one alone. Here's the preprint of the AAAI 2017 paper [2]. [1] http://www.conceptnet.io/ http://www.conceptnet.io/ [2] https://arxiv.org/pdf/1612.03975v1.pdf https://arxiv.org/pdf/1612.03975v1.pdf