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To do so technically would require a more sophisticated grammar, like a Chomsky Type 1 grammar, also termed a context-sensitive grammar. However, parser generators for context-free grammars often support the ability for user-written code to introduce limited amounts of context-sensitivity. (For example, upon encountering a variable declaration ...
Théâtre D'opéra Spatial, an image generated with Midjourney. Generative artificial intelligence ( generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3] [4] Generative AI models learn the patterns and structure of ...
Generative literature is poetry or fiction that is automatically generated, often using computers. It is a genre of electronic literature, and also related to generative art. John Clark 's Latin Verse Machine (1830-1843) is probably the first example of mechanised generative literature, [1] [2] while Christopher Strachey 's love letter ...
A famous example for lexical ambiguity is the following sentence: " Wenn hinter Fliegen Fliegen fliegen, fliegen Fliegen Fliegen hinterher. ", meaning "When flies fly behind flies, then flies fly in pursuit of flies." [39] [circular reference] It takes advantage of some German nouns and corresponding verbs being homonymous.
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Extend the generator classes through Perl programming. Only linking pdoc: overridable Jinja2 templates source code syntax highlighting, automatic cross-linking to symbol declarations Yes phpDocumentor: Smarty-based templates (1.x), Twig-based templates (2+) class inheritance diagrams
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Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. [2] It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers"). [3] [4] It is frequently used as the lex implementation together with Berkeley Yacc parser generator on BSD -derived operating systems (as both lex ...
Lexical tokenization is related to the type of tokenization used in Large language models (LLMs), but with two differences. First, lexical tokenization is usually based on a lexical grammar, whereas LLM tokenizers are usually probability -based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values.
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