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Topic: Stochastic context free grammar

    Note: these results are not from the primary (high quality) database.

grammar Grammar is the study of the rules governing the use of a language. The formal study of grammar is an important part of education from a young age through advanced learning, though the rules taught in schools are not a "grammar" in the sense most linguists use the term, as they are often prescriptive rather than descriptive. Grammar is part of the general study of language called linguistics. www.fact-library.com /grammar.html   (744 words)

Stochastic context-free grammar -- Facts, Info, and Encyclopedia article SCFGs extend context-free grammars in the same way that (additional info and facts about hidden Markov model) hidden Markov models extend (additional info and facts about regular grammar) regular grammars. The probability of a derivation (parse) is then the product of the probabilities of the productions used in that derivation; thus some derivations are more consistent with the stochastic grammar than others. This is equivalent to the probability of the SCFG generating the sequence, and is intuitively a measure of how consistent the sequence is with the given grammar. www.absoluteastronomy.com /encyclopedia/S/St/Stochastic_context-free_grammar.htm   (686 words)

BioMed Central Full text Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction Although one implementation of an SCFG mirror of the Zuker algorithm has been described [35], it used a structurally ambiguous grammar (it was not intended for secondary structure prediction per se; it was only used in summed Inside algorithm calculations where ambiguity doesn't matter, not in a CYK algorithm where ambiguity does matter). It can be tricky to write grammars that are structurally unambiguous, and it appears to be difficult to prove that they are so, except in simple cases. Once stacking correlations are included, four of the grammars (G6, G7, G8, and G6) have comparably good performance, at the cost of increasing parameter number to include the 16 × 16 parameters set for the three with stacking correlations. www.biomedcentral.com /1471-2105/5/71   (7989 words)

context In linguistics, context refers to the meaning of a word or phrase with regard to its place in the sentence and the topic being discussed. Context is a term with numerous meanings and shades of meaning. In computer science, context refers to the circumstances under which a device is being used, e.g. www.fact-library.com /context.html   (114 words)

Acquiring a Stochastic Context-Free Grammar from the Penn Treebank - Krotov, Gaizauskas, Wilks (ResearchIndex) Acquiring a stochastic context-free grammar from the Penn Treebank. Acquiring a Stochastic Context-Free Grammar from the Penn Treebank - Krotov, Gaizauskas, Wilks (ResearchIndex) Acquiring a Stochastic Context-Free Grammar from the Penn Treebank (1994) citeseer.ist.psu.edu /krotov94acquiring.html   (539 words)

R.C. Underwood's Masters Thesis Abstract In this thesis, stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of homologous RNA sequences--specifically, the small nuclear RNA sequences of the spliceosome. For each sequence, the grammar is used to computed a negative log-likelihood (NLL) that indicates how probable it is that the grammar would have produced that sequence. SCFGs generalize the hidden Markov models (HMMs) used in related work on protein and DNA to capture the sequences' common primary and secondary structure. reality.sgi.com /rcu/thesis/thesis.html   (544 words)

hitaka.html This means that for an inconsistent grammar, the expected applied number of a rewriting rule is not proportionate to the true value of the parameter attached to the rule, so it is hard to come up with a consistent parameter estimation for {inconsistent} stochastic context free grammars. Stochastic Context Free Grammars are widely used as a convenient probabilistic language model for speech recognition. We concentrated our research mainly on the characterization of { consistency} (or {inconsistency}) of stochastic context free grammar this year and solved almost all problems related to the subject. winnie.kuis.kyoto-u.ac.jp /taiwa/e-abst94/hitaka.html   (442 words)

Michael Paul Stewart Brown Publications Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. A model based on intersections of stochastic context free grammars is presented to allow for the modeling of RNA pseudoknot structures. This work presents the theory and application of stochastic context-free grammars (SCFGs) to biological sequence analysis and specifically to the problem of RNA secondary structure modeling. www.soe.ucsc.edu /%7Empbrown/mpsbPapers/index.html   (1677 words)

rnacad stochastic context-free grammar system RNACAD is a stochastic context-free grammar(SCFG) RNA modeling package that accounts for both primary and secondary structure information. SCFGs are roboust and allow arbitrary deviations from a pattern. One of its primary features is the use of a constraint system that increases its running performance considerably allowing large problems to be solved efficiently. www.cse.ucsc.edu /%7Empbrown/rnacad/index.html   (122 words)

SLG This combination of grammar and constraints is then converted into a standard stochastic context-free grammar for use in generating sentences or in making context dependent likelihood predictions of the sequence of words in a sentence. SLG allows the basic syntax of a grammar to be specified in context-free form and constraints to be applied atop this framework in a relatively natural fashion. Although context-free grammars are convenient for representing natural language syntax, they do not easily support the semantic and pragmatic constraints that make certain combinations of words or structures more likely than others. tedlab.mit.edu /~dr/SLG/index.html   (374 words)

How to Fill the Gap between Fractals and Stochastic Context-Free Grammars? Numerical results are used to compute the probabilities of a context-free grammar; since this grammar is inferred in the next block, detail of this previously unpublished work has been put in appendix A. Figure 1 shows the different blocks of the system - those being detailed in this paper are shown in bold style. Numerical results are used to determine the probabilities of assigning to the rules of a context-free grammar inferred from the set of trees. This work, excepting the computation of the stochastic grammar (detailed in 4), has been previously exposed in [4, 5, 3] and we recall below the main results which are useful for the understanding. www.complexity.org.au /ci/vol02/blanc/blanc.html   (2353 words)

Eddy lab :: Software COVE is an implementation of stochastic context free grammar methods for RNA sequence/structure analysis. All of our software is freely distributed as source code and documentation under open source licenses -- usually the "copyleft" of the GNU General Public License, but some people in the lab prefer other licenses such as the BSDL. RSEARCH aligns an RNA query to target sequences, using SCFG algorithms to score both secondary structure and primary sequence alignment simultaneously. www.genetics.wustl.edu /eddy/software   (836 words)

Parameter estimation for stochastic grammar In particular, existing algorithms for parameter estimation, such as Inside-outside, are applicable only to stochastic context-free grammar (SCFG) models for RNA stem-loops. Recently stochastic grammar models of RNA pseudoknots have been introduced to allow automated algorithms for pseudoknot prediction and profiling for structural homology recognition. In this paper, we introduce a new parameter estimation algorithm Upward-downward for the stochastic grammar model of RNA pseudoknots we have developed recently. www.cs.uga.edu /%7Ejizhen/rna.htm   (206 words)

General Departmental Seminar Series-March 8, 2001 We present a machine learning algorithm for refining the structure of a stochastic context--free grammar (SCFG). Structural refinement is important because most common SCFG learning methods set the probability parameters while leaving the structure of the grammar fixed. The heuristic identifies nonterminals in the model SCFG that appear to be performing the function of two or more nonterminals in the target SCFG, and the operator attempts to rectify this problem by introducing a new nonterminal. www.biostat.wisc.edu /generaladmin/seminars/dept030801.html   (185 words)

Precise n-gram Probabilities from Stochastic Context-free Grammars We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice. www.icsi.berkeley.edu /%7Estolcke/papers/acl94/paper-html.html   (72 words)

27-tk-abs.txt In this paper we restrict ourselves to stochastic context-free grammars which, while more analytically tractable than our SNet grammars, are more difficult than others previously considered by the GA community. In our approach, the production rules of a grammar are encoded as genes of a genome; this grammar is used as a recognizer of strings and assigned a fitness measure that reflects the probability that it captures the structure of a restricted sample of strings generated by a stochastic target language. We give results for two grammars whose nonstochastic equivalents have been used in previous studies. www.cs.ucsd.edu /~rik/foga4/Abstracts/27-tk-abs.txt   (146 words)

Research Questions in Grammar Specialisation and Disambiguation For instance, it is quite possible to envision stochastic context-free grammars of lexical dependency structures in which each of the non-terminal nodes in the stochastic context-free grammar relates to a word in the input sentence. Note, however, that the lack of expressiveness with respect to lexical dependencies is not an inherent property of stochastic context-free grammars, but rather a property of their typical use. In stochastic context-free grammars, grammar rules are augmented with probabilities. odur.let.rug.nl /~vannoord/alp/proposal/node16.html   (1024 words)

3aSP2 Statistical grammar inference. The need for a lexical and still hierarchical statistical language model is partially corroborated by preliminary experiments which show that SLTAG enable better statistical modeling than its hierarchical counterpart, stochastic context-free grammars. Stochastic lexicalized tree-adjoining grammar (SLTAG) has been recently suggested as the basis for training algorithm [Y. Schabes, in COLING '1992]. The experiments also show that SLTAG is capable of capturing lexical distributions as well as bigram models while maintaining the hierarchical nature of language. www.auditory.org /asamtgs/asa92nwo/3aSP/3aSP2.html   (286 words)

Unsupervised Induction of Stochastic Context-Free Grammars using Distributional Clustering Unsupervised Induction of Stochastic Context-Free Grammars using Distributional Clustering An algorithm is presented for learning a phrase-structure grammar from tagged text. The evaluation of unsupervised models is discussed, and results are presented when the algorithm has been trained on 12 million words of the British National Corpus. www.cnts.ua.ac.be /conll2001/abstracts/10512cla.html   (116 words)

ObjectSpaces Moore, Essa, "Recognizing Multitasked Activities using Stochastic Context-Free Grammar, In Proceedings of Workshop on Models versus Exemplars in Computer Vision, held in Conjunction with IEEE CVPR 2001, Kauai, Hawaii, December 2001. The complimentary usage of context will enable this architecture to recover from failures or inconsistencies that occur at either the lowes t or highest levels of abstraction in the system. Spatial context regarding the location of articles in the surroundings can be embedded into Activity zones to facilitate tracking and recognition. www.cc.gatech.edu /cpl/projects/objectspaces   (2212 words)

Parsing N-Best Lists of Handwritten Sentences The parsing of the sentences is achieved by a bottom-up chart parser for stochastic context-free grammars which produces the parse tree of the input sentence as well as the word tags. From a collection of corpora we extract the linguistic resources to build the lexicon, a word bigram model and the stochastic context-free grammar. For the generation of the N-best sentence ist an HMM-based recognizer including a bigram anguage model is used. csdl.computer.org /comp/proceedings/icdar/2003/1960/01/196010572abs.htm   (215 words)

Seminar:Parallel Training of a Stochastic Context Free Grammar for tRNA Secondary Structure Prediction The major task is to implement a Stochastic Context Free Grammar, which is a variation of Hidden Markov model suitable for searching gene in biological sequence databases, such as GenBank, and predicting the secondary structure of a sequence such as tRNA. Seminar:Parallel Training of a Stochastic Context Free Grammar for tRNA Secondary Structure Prediction The whole work is being implemented, using C language and MPI (message passing interface), on SHARC-NET. athena.uwindsor.ca /units/cs/CS.nsf/(MemberUIDLookupNewsAll)/DC1A8F884D74951A85256D02004D2BA9   (134 words)

Citations: Stochastic context-free grammars for modeling RNA - Sakakibara, Brown, Underwood, Mian, Haussler (SMEALSearch) - Pal,Rangaswamy,Giles,Debnath Citations: Stochastic context-free grammars for modeling RNA - Sakakibara, Brown, Underwood, Mian, Haussler (SMEALSearch) - Pal,Rangaswamy,Giles,Debnath Given a string like (its parse according to the grammar G uniquely determines pairs of matching parentheses. This paper is cited in the following contexts: smealsearch2.psu.edu /context/9909/0   (148 words)

Decoupling Context-Free Grammar from E-Commerce in Massive Multiplayer Online Role-Playing Games Gupta, a., Taylor, K., Smith, U., Harris, R., Dahl, O., Raman, W., and Qian, U. Contrasting context-free grammar and link-level acknowledgements with PastDozer. Johnson, B., and Raman, C. The impact of stochastic theory on probabilistic cryptography. Any extensive emulation of compact symmetries will clearly require that RAID [5,12,29,5] and the producer-consumer problem can connect to achieve this goal; Colin is no different. www.finis.info /decoupling.html   (2056 words)

Information and Control -- 1971 Complexity and unambiguity of context-free grammars and languages. A few remarks on the index of context-free grammars and languages. The generative capacity of transformational grammars of Ginsburg and Partee. theory.lcs.mit.edu /~iandc/ic71.html   (330 words)

Empirical Grammar Krotov, A., Gaizauskas, R., Wilks, Y.(1994) ``Acquiring a stochastic context-free grammar from the Penn Treebank'' In proceedings of the Third Conference on the Cognitive Science of Natural Language Processing, pages 79-86, Dublin. This is achieved by constructing a stochastic grammar (a grammar with probabilities attached to each rule) and trying to find the most probable grammar for a corpus. Our method is to combine inducing grammars empirically from a limited corpus with hand attached trees with a grammar produced from n-grams of part of speech tags over a much larger corpus. www.dcs.shef.ac.uk /nlp/groups/eg.html   (205 words)

Stanford NLP Group Or the software can be used simply as an accurate unlexicalized stochastic context-free grammar parser. The original version of this parser was mainly written by Dan Klein, with support code and linguistic grammar development by Christopher Manning. The strongest rain ever recorded in India shut down the financial hub of Mumbai, snapped communication lines, closed airports and forced thousands of people to sleep in their offices or walk home during the night, officials said today. nlp.stanford.edu /downloads/lex-parser.shtml   (700 words)

Information and Control -- 1975 Generalizations of regular sets and their applicatin to a study of context-free languages. On the syntactic structures of unrestricted grammars I. generative grammars and phrase structure grammars. The characterization by automata of certain classes of languages in the context sensititve area. theory.lcs.mit.edu /~iandc/ic75.html   (340 words)

MERL – TR1993-012 – Stochastic Lexicalized Context-Free Grammar Stochastic lexicalized context-free grammar (SLCFG) is an attractive compromise between the parsing efficiency of stochastic context-free grammar (SCFG) and the lexical sensitivity of stochastic lexicalized tree-adjoining grammar (SLTAG). SLCFG is a restricted form of SLTAG that can only generate context-free languages and can be parsed in cubic time. However, SLCFG retains the lexical sensitivity of SLTAG and is therefore a much better basis for capturing distributional information about words than SCFG. www.merl.com /reports/TR93-12   (95 words)

LINGUIST List 14.1254: Phonology/Morphology; Computational Ling Yet long term dependencies, palindromic structures, parenthesis are all internal structures that may appear in a wide range of applications and are better described by context free grammars. The purpose of the workshop is to provide a forum specific to this question, enabling researchers to present their most recent results over the issue of learning context-free grammars. All paper presenters will be provided free accomodation for up to four nights in a centrally located hotel. www.ling.ed.ac.uk /linguist/issues/14/14-1254.html   (574 words)

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