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Natural Language Generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. — Wikipedia
Is NLG about building meaningful sentences, reports, etc.? Can NLG build valid dictionary words as well? For example, without consulting/reading from an English (or any language) dictionary, can an algorithm generate such words?
Outside of context of NLG (thus not a direct answer to your whole question, but an answer to your question's title): Generating words from a character-level model has been done using RNNs exposed to large corpora of text, such as Wikipedia content, and trained to predict text character-by-character.
Used to generate content, the model is normally fed a few starting characters and asked to predict the next one. A choice is made from its most-likely predictions and fed back to it to continue the sequence.
Here is a blog showing some examples trained on some Shakespear and Wikipedia.
Such a network can and does generate nonsense words, although they are often fitting and might read like e.g. a noun or verb as you could expect depending on context. The sentence structure and grammar can come out sort of right, but the semantic content is usually complete gibberish.
You are looking for Recurrent Neural Networks for character-level language models.
Have a look at this. https://github.com/karpathy/char-rnn
