Embedded Stochastic Syntactic Processes: A Class of Stochastic Grammars Equivalent by Embedding to a Markov Process
| Autor: | Langford B. White, Francesco Carravetta |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
Markov random field Stochastic process Computer science Markov processes Parse tree Aerospace Engineering Markov process Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Statistical model symbols.namesake TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Rule-based machine translation Stochastic Grammar symbols Graphical model Electrical and Electronic Engineering Filtering Hidden Markov model |
| Zdroj: | IEEE transactions on aerospace and electronic systems 57 (2021): 1996–2005. doi:10.1109/TAES.2021.3083419 info:cnr-pdr/source/autori:Carravetta F.; White L.B./titolo:Embedded stochastic syntactic processes: A class of stochastic grammars equivalent by embedding to a Markov process/doi:10.1109%2FTAES.2021.3083419/rivista:IEEE transactions on aerospace and electronic systems/anno:2021/pagina_da:1996/pagina_a:2005/intervallo_pagine:1996–2005/volume:57 |
| ISSN: | 2371-9877 0018-9251 |
| Popis: | This article addresses the problem of suitably defining statistical models of languages derived from context-free grammars (CFGs), where the observed strings may be corrupted by noise or other mechanisms. This article uses the concept of a stochastic syntactic process (SSP), which we have introduced in previous work. An SSP is a stochastic process taking values in the set of all parse trees of a CFG. Inference problems such as estimating a parse tree for “noisy” processes are of obvious significance, particularly in the motivating example of metalevel target tracking. This article demonstrates that by careful application of the theory of probability, an SSP can be embedded into a Markov random field (MRF), thus opening up the possibility of the application of advanced machine learning algorithms based on graphical models to inference problems involving sophisticated target behavior at the “meta” level. This article provides a simple example of how a simple CFG can be embedded in an MRF. Extensions to context-sensitive grammars are discussed. |
| Databáze: | OpenAIRE |
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