403 research outputs found
On certain formal properties of grammars
A grammar can be regarded as a device that enumerates the sentences of a language. We study a sequence of restrictions that limit grammars first to Turing machines, then to two types of system from which a phrase structure description of the generated language can be drawn, and finally to finite state Markov sources (finite automata). These restrictions are shown to be increasingly heavy in the sense that the languages that can be generated by grammars meeting a given restriction constitute a proper subset of those that can be generated by grammars meeting the preceding restriction. Various formulations of phrase structure description are considered, and the source of their excess generative power over finite state sources is investigated in greater detail
Proof of Church's Thesis
We prove that if our calculating capability is that of a universal Turing
machine with a finite tape, then Church's thesis is true. This way we
accomplish Post (1936) program.Comment: 6 page
Optimal alignment algorithm for context-sensitive hidden Markov models
The hidden Markov model is well-known for its efficiency in modeling short-term dependencies between adjacent samples. However, it cannot be used for modeling longer-range interactions between symbols that are distant from each other. In this paper, we introduce the concept of context-sensitive HMM that is capable of modeling strong pairwise correlations between distant symbols. Based on this model, we propose a polynomial-time algorithm that can be used for finding the optimal state sequence of an observed
symbol string. The proposed model is especially useful in modeling palindromes, which has an important application in RNA secondary structure analysis
Building Domain Specific Languages for Voice Recognition Applications
This paper presents a method of implementing the voice recognition for the control of software applications. The solutions proposed are based on transforming a subset of the natural language in commands recognized by the application using a formal language defined by the means of a context free grammar. At the end of the paper is presented the modality of integration of voice recognition and of voice synthesis for the Romanian language in Windows applications.voice recognition, formal languages, context free grammars, text to speech
HMM with auxiliary memory: a new tool for modeling RNA structures
For a long time, proteins have been believed to perform most of the important functions in all cells. However, recent results in genomics have revealed that many RNAs that do not encode proteins play crucial roles in the cell machinery. The so-called ncRNA genes that are transcribed into RNAs but not translated into proteins, frequently conserve their secondary structures more than they conserve their primary sequences. Therefore, in order to identify ncRNA genes, we have to take the secondary structure of RNAs into consideration. Traditional approaches that are mainly based on base-composition statistics cannot be used for modeling and identifying such structures and models with more descriptive power are required. In this paper, we introduce the concept of context-sensitive HMMs, which is capable of describing pairwise interactions between distant symbols. It is demonstrated that the proposed model can efficiently model various RNA secondary structures that are frequently observed
Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures
The presence of Long Distance Dependencies (LDDs) in sequential data poses
significant challenges for computational models. Various recurrent neural
architectures have been designed to mitigate this issue. In order to test these
state-of-the-art architectures, there is growing need for rich benchmarking
datasets. However, one of the drawbacks of existing datasets is the lack of
experimental control with regards to the presence and/or degree of LDDs. This
lack of control limits the analysis of model performance in relation to the
specific challenge posed by LDDs. One way to address this is to use synthetic
data having the properties of subregular languages. The degree of LDDs within
the generated data can be controlled through the k parameter, length of the
generated strings, and by choosing appropriate forbidden strings. In this
paper, we explore the capacity of different RNN extensions to model LDDs, by
evaluating these models on a sequence of SPk synthesized datasets, where each
subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple
languages, the presence of LDDs does have significant impact on the performance
of recurrent neural architectures, thus making them prime candidate in
benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201
An overview of the role of context-sensitive HMMs in the prediction of ncRNA genes
Non-coding RNAs (ncRNA) are RNA molecules that function in the cells without being translated into proteins. In recent years, much evidence has been found that ncRNAs play a crucial role in various biological processes. As a result, there has been an increasing interest in the prediction of ncRNA genes. Due to the conserved secondary structure in ncRNAs, there exist pairwise dependencies between distant bases. These dependencies cannot be effectively modeled using traditional HMMs, and we need a more complex model such as the context-sensitive HMM (csHMM). In this paper, we overview the role of csHMMs in the RNA secondary structure analysis and the prediction of ncRNA genes. It is demonstrated that the context-sensitive HMMs can serve as an efficient framework for these purposes
Linear Parsing Expression Grammars
PEGs were formalized by Ford in 2004, and have several pragmatic operators
(such as ordered choice and unlimited lookahead) for better expressing modern
programming language syntax. Since these operators are not explicitly defined
in the classic formal language theory, it is significant and still challenging
to argue PEGs' expressiveness in the context of formal language theory.Since
PEGs are relatively new, there are several unsolved problems.One of the
problems is revealing a subclass of PEGs that is equivalent to DFAs. This
allows application of some techniques from the theory of regular grammar to
PEGs. In this paper, we define Linear PEGs (LPEGs), a subclass of PEGs that is
equivalent to DFAs. Surprisingly, LPEGs are formalized by only excluding some
patterns of recursive nonterminal in PEGs, and include the full set of ordered
choice, unlimited lookahead, and greedy repetition, which are characteristic of
PEGs. Although the conversion judgement of parsing expressions into DFAs is
undecidable in general, the formalism of LPEGs allows for a syntactical
judgement of parsing expressions.Comment: Parsing expression grammars, Boolean finite automata, Packrat parsin
A universal context-free grammar
In this report we show that, for each alphabet ÎŁ, there exists a context-free grammar G which satisfies the property that for each context-free language L â ÎŁ* a regular control set C can be found such that LC(G) = L
- âŚ