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2. Artificial grammar learning - Wikipedia

   en.wikipedia.org/wiki/Artificial_grammar_learning

   The AI programs first adapted to simulate both natural and artificial grammar learning used the following basic structure: Given A set of grammatical sentences from some language. Find A procedure for recognizing and/or generating all grammatical sentences in that language. An early model for AI grammar learning is Wolff's SNPR System.

3. Garden-path sentence - Wikipedia

   en.wikipedia.org/wiki/Garden-path_sentence

   The garden-path sentence effect occurs when the sentence has a phrase or word with an ambiguous meaning that the reader interprets in a certain way and, when they read the whole sentence, there is a difference in what has been read and what was expected. The reader must then read and evaluate the sentence again to understand its meaning.

4. Grammar checker - Wikipedia

   en.wikipedia.org/wiki/Grammar_checker

   A grammar checker will find each sentence in a text, look up each word in the dictionary, and then attempt to parse the sentence into a form that matches a grammar. Using various rules, the program can then detect various errors, such as agreement in tense, number, word order, and so on. It is also possible to detect some stylistic problems ...

5. Large language model - Wikipedia

   en.wikipedia.org/wiki/Large_language_model

   Some examples of commonly used question answering datasets include TruthfulQA, Web Questions, TriviaQA, and SQuAD. [127] Evaluation datasets may also take the form of text completion, having the model select the most likely word or sentence to complete a prompt, for example: "Alice was friends with Bob. Alice went to visit her friend, ____". [1]

6. Generative grammar - Wikipedia

   en.wikipedia.org/wiki/Generative_grammar

   For example, generative theories generally provide competence-based explanations for why English speakers would judge the sentence in (1) as odd. In these explanations, the sentence would be ungrammatical because the rules of English only generate sentences where demonstratives agree with the grammatical number of their associated noun. [14]

7. Autocorrection - Wikipedia

   en.wikipedia.org/wiki/Autocorrection

   Typically, the prankster will set the victim's word processing software to replace an extremely common word with a humorous absurdity, or an incorrectly spelled version of the original word. (example: Replacing "groceries" with "geography" to get a sentence such as "I'm going to the store to buy some geography. I mean geography. Geography.