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Concept
Stochastic grammar
Summary
A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality:
Stochastic context-free grammar
Statistical parsing
Data-oriented parsing
Hidden Markov model
Estimation theory
The grammar is realized as a language model. Allowed sentences are stored in a database together with the frequency how common a sentence is. Statistical natural language processing uses stochastic, probabilistic and statistical methods, especially to resolve difficulties that arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses. Methods for disambiguation often involve the use of corpora and Markov models. "A probabilistic model consists of a non-probabilistic model plus some numerical quantities; it is not true that probabilistic models are inherently simpler or less structural than non-probabilistic models."
A probabilistic method for rhyme detection is implemented by Hirjee & Brown in their study in 2013 to find internal and imperfect rhyme pairs in rap lyrics. The concept is adapted from a sequence alignment technique using BLOSUM (BLOcks SUbstitution Matrix). They were able to detect rhymes undetectable by non-probabilistic models.
Official source
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Related publications (12)
The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony
Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style.
The main topic of this study is grammar induction for harmony --- the process of learning structural principles f ...
EPFL2020Harmonic Syntax in Time: Rhythm Improves Grammatical Models of Harmony
Martin Alois Rohrmeier, Daniel Harasim
Music is hierarchically structured, both in how it is perceived by listeners and how it is composed. Such structure can be elegantly captured using probabilistic grammatical models similar to those used to study natural language. They address the complexit ...
ISMIR2019Visualization, Adaptation, and Transformation of Procedural Grammars
Procedural shape grammars are powerful tools for the automatic generation of highly detailed 3D content from a set of descriptive rules. It is easy to encode variations in stochastic and parametric grammars, and an uncountable number of models can be gener ...
EPFL2017Related people (2)
Related concepts (3)
Probabilistic context-free grammar
Grammar theory to model symbol strings originated from work in computational linguistics aiming to understand the structure of natural languages. Probabilistic context free grammars (PCFGs) have been applied in probabilistic modeling of RNA structures almost 40 years after they were introduced in computational linguistics. PCFGs extend context-free grammars similar to how hidden Markov models extend regular grammars. Each production is assigned a probability.
Computational linguistics
Computational linguistics has since 2020s became a near-synonym of either natural language processing or language technology, with deep learning approaches, such as large language models, overperforming the specific approaches previously used in the field. The field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English.
Natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.