computer science

Semantic neural network

Semantic neural network is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Semantic neural network rather than just read about it. In short: Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network. There are limitations to a link topology for the von Neumann’s network but SNN accept a case without these limitations.

Key takeaways

Semantic neural network belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Semantic neural network to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Semantic neural network from memory before moving on to harder problems.

Reference excerpt

Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network. There are limitations to a link topology for the von Neumann’s network but SNN accept a case without these limitations. Only logical values can be processed, but SNN accept that fuzzy values can be processed too. All neurons into the von Neumann network are synchronized by tacts. For further use of self-synchronizing circuit technique SNN accepts neurons can be self-running or synchronized. In contrast to the von Neumann network there are no limitations for topology of neurons for semantic networks. It leads to the impossibility of relative addressing of neurons as it was done by von Neumann. In this case an absolute readdressing should be used. Every neuron should have a unique identifier that would provide a direct access to another neuron. Of course, neurons interacting by axons-dendrites should have each other's identifiers. An absolute readdressing can be modulated by using neuron specificity as it was realized for biological neural networks. There’s no description for self-reflectiveness and self-modification abilities into the initial description of semantic networks [Dudar Z.V., Shuklin D.E., 2000]. But in [Shuklin D.E. 2004] a conclusion had been drawn about the necessity of introspection and self-modification abilities in the system. For maintenance of these abilities a concept of pointer to neuron is provided. Pointers represent virtual connections between neurons. In this model, bodies and signals transferring through the neurons connections represent a physical body, and virtual connections between neurons are representing an astral body. It is proposed to create models of artificial neuron networks on the basis of virtual machine supporting the opportunity for paranormal effects. SNN is generally used for natural language processing.

Related models Computational creativity Semantic hashing Semantic Pointer Architecture Sparse distributed memory

References

Neumann, J., 1966. Theory of self-reproducing automata, edited and completed by Arthur W. Burks. - University of Illinois press, Urbana and London Dudar Z.V., Shuklin D.E., 2000. Implementation of neurons for semantic neural nets that’s understanding texts in natural language. In Radio-electronika i informatika KhTURE, 2000. No 4. Р. 89-96. Shuklin D.E., 2004. The further development of semantic neural network models. In Artificial Intelligence, Donetsk, "Nauka i obrazovanie" Institute of Artificial Intelligence, Ukraine, 2004, No 3. P. 598-606

Shuklin D.E. The Structure of a Semantic Neural Network Extracting the Meaning from a Text, In Cybernetics and Systems Analysis, Volume 37, Number 2, 4 March 2001, pp. 182–186(5) [1] Shuklin D.E. The Structure of a Semantic Neural Network Realizing Morphological and Syntactic Analysis of a Text, In Cybernetics and Systems Analysis, Volume 37, Number 5, September 2001, pp. 770–776(7) Shuklin D.E. Realization of a Binary Clocked Linear Tree and Its Use for Processing Texts in Natural Languages, In Cybernetics and Systems Analysis, Volume 38, Number 4, July 2002, pp. 503–508(6)

Worked examples

Example 1 — a first encounter with Semantic neural network

Start with the simplest possible case. Write down what Semantic neural network claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Semantic neural network before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Semantic neural network ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Semantic neural network

In research: Semantic neural network appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Semantic neural network in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Semantic neural network is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial neural networks, Natural language processing, so understanding it makes those chapters shorter.
In everyday life: Look for Semantic neural network outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Semantic neural network in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Semantic neural network means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Semantic neural network out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantic neural network in simple terms?

Why does Semantic neural network matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Semantic neural network?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Semantic neural network.