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. 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 machine learning. A probabilistic grammar's validity is constrained by context of its training dataset.
PCFGs have application in areas as diverse as natural language processing to the study the structure of RNA molecules and design of programming languages. 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.
Derivation: The process of recursive generation of strings from a grammar.
Parsing: Finding a valid derivation using an automaton.
Parse Tree: The alignment of the grammar to a sequence.
An example of a parser for PCFG grammars is the pushdown automaton. The algorithm parses grammar nonterminals from left to right in a stack-like manner. This brute-force approach is not very efficient. In RNA secondary structure prediction variants of the Cocke–Younger–Kasami (CYK) algorithm provide more efficient alternatives to grammar parsing than pushdown automata. Another example of a PCFG parser is the Stanford Statistical Parser which has been trained using Treebank.
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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.
L'algorithme de Viterbi, d'Andrew Viterbi, permet de corriger, dans une certaine mesure, les erreurs survenues lors d'une transmission à travers un canal bruité. Son utilisation s'appuie sur la connaissance du canal bruité, c'est-à-dire la probabilité qu'une information ait été modifiée en une autre, et permet de simplifier radicalement la complexité de la recherche du message d'origine le plus probable. D'exponentielle, cette complexité devient linéaire.
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We teach the fundamental aspects of analyzing and interpreting computer languages, including the techniques to build compilers. You will build a working compiler from an elegant functional language in
Introduit l'algorithme d'analyse CYK pour les grammaires sans contexte, expliquant sa gestion de l'ambiguïté et de l'importance dans l'analyse des grammaires générales.
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EPFL2024
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In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...
Le cours suivi propose une initiation aux concepts de base de la programmation impérative tels que : variables, expressions, structures de contrôle, fonctions/méthodes, en les illustrant dans la synta
Le cours suivi propose une introduction aux concepts de base de la programmation orientée objet tels que : encapsulation et abstraction, classes/objets, attributs/méthodes, héritage, polymorphisme, ..
The purpose of this MOOC is to offer a complementary capstone project to our existing MOOCs in introduction to programming. This will offer the students the possibility to both stabilize the already a