# Language Model **Domain:** Natural Language Processing / Artificial Intelligence **Doc Type:** Canonical Concept Node **Maturity:** Developed **Related:** [[wiki/Natural Language Processing|Natural Language Processing]], [[wiki/Trigger-Based Language Modeling|Trigger-Based Language Modeling]], [[wiki/Distributional Semantics|Distributional Semantics]], [[wiki/Machine Learning|Machine Learning]], [[wiki/Formal Grammar|Formal Grammar]] --- ## Definition **A Language Model assigns probabilities or scores to linguistic sequences, continuations or structures.** Models range from count-based n-grams and trigger models to neural systems trained over large text collections. A language model captures regularities in usage; it does not automatically possess an explicit theory of meaning, truth or authorship. Fluent continuation can coexist with weak source attribution and uncertain grounding. ## Symbolic Language Engine Context The early project used corpus-derived trigger relations alongside explicit phonological, grammatical and semantic structures. It was therefore not one unified language model in the contemporary sense. It was a coordinated set of typed models and indexes. [[wiki/Semantic-First Constrained Generation|Semantic-First Constrained Generation]] reverses the usual procedural-poetry order. A semantic trajectory supplies what the system is trying to express, while grammar, rhyme, meter and corpus evidence constrain its realization. ## Contemporary Bridge A local neural language model could expand candidate generation in a rebuilt [[wiki/VersePerfect|VersePerfect]], but its outputs should remain labeled as model suggestions rather than corpus attestations or symbolic deductions. ## Key Insight **A language model predicts linguistic possibility. Additional structures are required to identify relation type, evidence source and intended meaning.** ## See Also [[wiki/Natural Language Processing|Natural Language Processing]], [[wiki/Trigger-Based Language Modeling|Trigger-Based Language Modeling]], [[wiki/Distributional Semantics|Distributional Semantics]], [[wiki/Word Embeddings|Word Embeddings]], [[wiki/Neuro-Symbolic AI|Neuro-Symbolic AI]], [[wiki/Provenance-Sensitive Multiplicity|Provenance-Sensitive Multiplicity]] ## Sources / Provenance - Primary project account: [[projects/Ten Years Building a Symbolic Language Engine|Ten Years Building a Symbolic Language Engine]]. - Daniel Jurafsky and James H. Martin, *Speech and Language Processing*. - Historical n-gram, trigger and neural language-modeling literature.