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You searched for subject:(N gram tree). Showing records 1 – 3 of 3 total matches.

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Louisiana State University

1. Poddar, Anindya. Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction.

Degree: PhD, Computer Sciences, 2011, Louisiana State University

Preprocessing of database is inevitable to extract information from large databases like biological sequences of gene or protein. Discovery of patterns becomes very time efficient when we preprocess the database in the form suffix array. Due to inherent organization of data in suffix array and it’s secondary data structure longest common prefix (LCP) array (Manber and Myers 1990) only a limited portion of the database is accessed during the searching operation which results in outcome of plenty of information in very less amount of time depending on the size of the database. Unlike exact pattern matching here we preprocess the database instead of pattern. We found suffix and LCP array as a perfect tool to compute N-grams (substring) in various dimensions. Since past couple of decades there has been significant research on construction of suffix and LCP array. Comparatively the research of properly utilizing this prospective data structures to retrieve the substring information from various perspectives have remained almost unfocussed. Our main focus in this work was to develop a number of algorithms for computing present and missing N-grams in a text in linear time and present them non-redundantly for large databases. Finding information of present and missing N-grams and their time efficient non-redundant representation in large genome sequences can lead to new discovery in biology in the future. We have implemented and applied all our algorithms on various genome and proteome sequences and found interesting results. They were also tested for performance and other hardware parameter measurements on various platforms in order to suggest appropriate architecture for this kind of application.

Subjects/Keywords: String; Proteome; N-gram tree; N-gram tree of text; Substring; Pattern; Genome; N-gram; Suffix Tree; Suffix Array

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APA (6th Edition):

Poddar, A. (2011). Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04152011-174740 ; https://digitalcommons.lsu.edu/gradschool_dissertations/490

Chicago Manual of Style (16th Edition):

Poddar, Anindya. “Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction.” 2011. Doctoral Dissertation, Louisiana State University. Accessed November 14, 2019. etd-04152011-174740 ; https://digitalcommons.lsu.edu/gradschool_dissertations/490.

MLA Handbook (7th Edition):

Poddar, Anindya. “Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction.” 2011. Web. 14 Nov 2019.

Vancouver:

Poddar A. Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction. [Internet] [Doctoral dissertation]. Louisiana State University; 2011. [cited 2019 Nov 14]. Available from: etd-04152011-174740 ; https://digitalcommons.lsu.edu/gradschool_dissertations/490.

Council of Science Editors:

Poddar A. Efficient Substring Discovery Using Suffix, LCP Array and Algorithm-Architecture Interaction. [Doctoral Dissertation]. Louisiana State University; 2011. Available from: etd-04152011-174740 ; https://digitalcommons.lsu.edu/gradschool_dissertations/490


Georgia Tech

2. Mallikarjuna, Trishul. Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework.

Degree: MS, Center for Music Technology, 2010, Georgia Tech

Elements of continuous variation such as tremolo, vibrato and portamento enable dimensions of their own in expressive melodic music in styles such as in Indian Classical Music. There is published work on parametrically modeling some of these elements individually, and to apply the modeled parameters to automatically generated musical notes in the context of machine musicianship, using simple rule-based mappings. There have also been many systems developed for generative musical accompaniment using probabilistic models of discrete musical elements such as MIDI notes and durations, many of them inspired by computational research in linguistics. There however doesn't seem to have been a combined approach of parametrically modeling expressive elements in a probabilistic framework. This documents presents a real-time computational framework that uses a multi-attribute trie / n-gram structure to model parameters like frequency, depth and/or lag of the expressive variations such as vibrato and portamento, along with conventionally modeled elements such as musical notes, their durations and metric positions in melodic audio input. This work proposes storing the parameters of expressive elements as metadata in the individual nodes of the traditional trie structure, along with the distribution of their probabilities of occurrence. During automatic generation of music, the expressive parameters as learned in the above training phase are applied to the associated re-synthesized musical notes. The model is aimed at being used to provide automatic melodic accompaniment in a performance scenario. The parametric modeling of the continuous expressive elements in this form is hypothesized to be able to capture deeper temporal relationships among musical elements and thereby is expected to bring about a more expressive and more musical outcome in such a performance than what has been possible using other works of machine musicianship using only static mappings or randomized choice. A system was developed on Max/MSP software platform with this framework, which takes in a pitched audio input such as human singing voice, and produces a pitch track which may be applied to synthesized sound of a continuous timbre. The system was trained and tested with several vocal recordings of North Indian Classical Music, and a subjective evaluation of the resulting audio was made using an anonymous online survey. The results of the survey show the output tracks generated from the system to be as musical and expressive, if not more, than the case where the pitch track generated from the original audio was directly rendered as output, and also show the output with expressive elements to be perceivably more expressive than the version of the output without expressive parameters. The results further suggest that more experimentation may be required to conclude the efficacy of the framework employed in relation to using randomly selected parameter values for the expressive elements. This thesis presents the scope, context, implementation details and results… Advisors/Committee Members: Chordia, Parag (Committee Chair), Freeman, Jason (Committee Co-Chair), Weinberg, Gil (Committee Co-Chair).

Subjects/Keywords: Portavibratremo; Octave; Tri-octave; Position-in-bar; PIB; Position in bar; Octave errors; Metadata; Suffix tree; Parag chordia; Pitch tracking; Parametric modeling; MSP; Pitch classportamento lag; Vibrato rate; N-gram; Gtcmt; Trishul mallikarjuna; Pitch track; Markov tree; Max; Markov; Trie; Probabilistic modeling; Tremolo; Vibrato depth; Vibrato; Muti-attribute; Portamento; Tremolo rate; Tremolo depth; Music; Markov processes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mallikarjuna, T. (2010). Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37190

Chicago Manual of Style (16th Edition):

Mallikarjuna, Trishul. “Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework.” 2010. Masters Thesis, Georgia Tech. Accessed November 14, 2019. http://hdl.handle.net/1853/37190.

MLA Handbook (7th Edition):

Mallikarjuna, Trishul. “Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework.” 2010. Web. 14 Nov 2019.

Vancouver:

Mallikarjuna T. Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework. [Internet] [Masters thesis]. Georgia Tech; 2010. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/1853/37190.

Council of Science Editors:

Mallikarjuna T. Towards expressive melodic accompaniment using parametric modeling of continuous musical elements in a multi-attribute prediction suffix trie framework. [Masters Thesis]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37190

3. Wagner, Joachim. Detecting grammatical errors with treebank-induced, probabilistic parsers.

Degree: School of Computing, 2012, Dublin City University

Today's grammar checkers often use hand-crafted rule systems that define acceptable language. The development of such rule systems is labour-intensive and has to be repeated for each language. At the same time, grammars automatically induced from syntactically annotated corpora (treebanks) are successfully employed in other applications, for example text understanding and machine translation. At first glance, treebank-induced grammars seem to be unsuitable for grammar checking as they massively over-generate and fail to reject ungrammatical input due to their high robustness. We present three new methods for judging the grammaticality of a sentence with probabilistic, treebank-induced grammars, demonstrating that such grammars can be successfully applied to automatically judge the grammaticality of an input string. Our best-performing method exploits the differences between parse results for grammars trained on grammatical and ungrammatical treebanks. The second approach builds an estimator of the probability of the most likely parse using grammatical training data that has previously been parsed and annotated with parse probabilities. If the estimated probability of an input sentence (whose grammaticality is to be judged by the system) is higher by a certain amount than the actual parse probability, the sentence is flagged as ungrammatical. The third approach extracts discriminative parse tree fragments in the form of CFG rules from parsed grammatical and ungrammatical corpora and trains a binary classifier to distinguish grammatical from ungrammatical sentences. The three approaches are evaluated on a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting common grammatical errors into the British National Corpus. The results are compared to two traditional approaches, one that uses a hand-crafted, discriminative grammar, the XLE ParGram English LFG, and one based on part-of-speech n-grams. In addition, the baseline methods and the new methods are combined in a machine learning-based framework, yielding further improvements. Advisors/Committee Members: Foster, Jennifer, van Genabith, Josef, IRCSET.

Subjects/Keywords: Computational linguistics; Machine learning; Artificial intelligence; Language; Linguistics; grammar checker; error detection; natural language processing; probabilistic grammar; precision grammar; decision tree learning; ROC curve; voting classifier, n-gram language models; learner corpus; error corpora

…168 6.2.1 6.2.2 Part-of-Speech n-gram Features… …Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 C.3.1 n-gram… …Accuracy results for the n-gram method on training data (on which the selection of optimal… …the n-gram method using the union of optimal parameter sequences of the cross-validation… …grammaticality judgements with information from the distorted treebank and n-gram methods . 166 6.3… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wagner, J. (2012). Detecting grammatical errors with treebank-induced, probabilistic parsers. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/16776/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wagner, Joachim. “Detecting grammatical errors with treebank-induced, probabilistic parsers.” 2012. Thesis, Dublin City University. Accessed November 14, 2019. http://doras.dcu.ie/16776/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wagner, Joachim. “Detecting grammatical errors with treebank-induced, probabilistic parsers.” 2012. Web. 14 Nov 2019.

Vancouver:

Wagner J. Detecting grammatical errors with treebank-induced, probabilistic parsers. [Internet] [Thesis]. Dublin City University; 2012. [cited 2019 Nov 14]. Available from: http://doras.dcu.ie/16776/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wagner J. Detecting grammatical errors with treebank-induced, probabilistic parsers. [Thesis]. Dublin City University; 2012. Available from: http://doras.dcu.ie/16776/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.