Probabilistic context-free grammar

<p>In <a href="/facts/Theoretical_linguistics/15wbqaY1">theoretical linguistics</a> and <a href="/facts/Computational_linguistics/XmegJ6LC">computational linguistics</a>, probabilistic context free grammars (PCFGs) extend <a href="/facts/Context-free_grammar/yu1FFdK4">context-free grammars</a>, similar to how <a href="/facts/Hidden_Markov_model/ur1zTAhP">hidden Markov models</a> extend <a href="/facts/Regular_grammar/EFCPJB9K">regular grammars</a>. Each <a href="/facts/Formal_grammar/QLxaQNAz">production</a> is assigned a probability. The probability of a derivation (parse) is the product of the probabilities of the productions used in that derivation. These probabilities can be viewed as parameters of the model, and for large problems it is convenient to learn these parameters via <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>. A probabilistic grammar's validity is constrained by context of its training dataset.
</p><p>PCFGs originated from <a href="/facts/Grammar_theory/6PxSV98p">grammar theory</a>, and have application in areas as diverse as <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a> to the study the structure of <a href="/facts/RNA/HxPeNfNj">RNA</a> molecules and design of <a href="/facts/Programming_language/cWTYbgWa">programming languages</a>. Designing efficient PCFGs has to weigh factors of scalability and generality. Issues such as grammar ambiguity must be resolved. The grammar design affects results accuracy. Grammar parsing algorithms have various time and memory requirements.
</p>

Probabilistic context-free grammar open-in-new

Probabilistic context-free grammar