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I 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 yo
by DonaldFisk 10y ago
I 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