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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2011 - 13

Domain Independent Vocabulary Generation and Its Use in Category-based Small Footprint Language Model

KIM, K.-H. See more information about KIM, K.-H. on SCOPUS See more information about KIM, K.-H. on IEEExplore See more information about KIM, K.-H. on Web of Science, KIM, J.-H. See more information about KIM, J.-H. on SCOPUS See more information about KIM, J.-H. on SCOPUS See more information about KIM, J.-H. on Web of Science
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Download PDF pdficon (639 KB) | Citation | Downloads: 1,116 | Views: 2,949

Author keywords
natural language processing, speech recognition

References keywords
language(16), speech(12), spoken(5), recognition(5), processing(5), modeling(5), model(5), vocabulary(4), statistical(4), gram(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-02-27
Volume 11, Issue 1, Year 2011, On page(s): 77 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.01013
Web of Science Accession Number: 000288761800013
SCOPUS ID: 79955973325

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The work in this paper pertains to domain independent vocabulary generation and its use in category-based small footprint Language Model (LM). Two major constraints of the conventional LMs in the embedded environment are memory capacity limitation and data sparsity for the domain-specific application. This data sparsity adversely affects vocabulary coverage and LM performance. To overcome these constraints, we define a set of domain independent categories using a Part-Of-Speech (POS) tagged corpus. Also, we generate a domain independent vocabulary based on this set using the corpus and knowledge base. Then, we propose a mathematical framework for a category-based LM using this set. In this LM, one word can be assigned assign multiple categories. In order to reduce its memory requirements, we propose a tree-based data structure. In addition, we determine the history length of a category n-gram, and the independent assumption applying to a category history generation. The proposed vocabulary generation method illustrates at least 13.68% relative improvement in coverage for a SMS text corpus, where data are sparse due to the difficulties in data collection. The proposed category-based LM requires only 215KB which is 55% and 13% compared to the conventional category-based LM and the word-based LM, respectively. It successively improves the performance, achieving 54.9% and 60.6% perplexity reduction compared to the conventional category-based LM and the word-based LM in terms of normalized perplexity.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 717 TCR
SCOPUS® Citations for all references: 1,337 TCR

Web of Science® Average Citations per reference: 27 ACR
SCOPUS® Average Citations per reference: 50 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-02-12 16:59 in 61 seconds.

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