Classification Grammatical Error
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C3S2E '14 Proceedings of the 2014 International C* Conference on Computer Science & Software Engineering Article grammatical classification of sentences No. 6 ACM New York, NY, USA ©2014 tableofcontents ISBN:
Grammatical Error Examples
978-1-4503-2712-1 doi>10.1145/2641483.2641527 2014 Article Tutorial Research Refereedlimited Bibliometrics ·Downloads (6 Weeks): 5 ·Downloads (12 grammatical error checker Months): 22 ·Downloads (cumulative): 53 ·Citation Count: 0 Recent authors with related interests Concepts in this article powered by Concepts inClassification and grammatical error definition Generation of Grammatical Errors Statistical classification In machine learning and statistics, classification is the problem of identifying which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. The individual observations are
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analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g. morefromWikipedia Stochastic grammar 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 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. morefromWikipedia Pattern recognition In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam" or "non-spam"). However, pattern recognition is a more general prob
ChapterText, Speech and Dialogue Volume 7499 of the series Lecture Notes in Computer Science pp 616-623Sentence Classification with Grammatical Errors and grammatical error symbols Those Out of Scope of Grammar Assumption for Dialogue-Based CALL SystemsYu NagaiAffiliated withFaculty of Science and Engineering, Doshisha University, Tomohisa SenzaiAffiliated withFaculty grammatical error or grammar error of Science and Engineering, Doshisha University, Seiichi YamamotoAffiliated withFaculty of Science and Engineering, Doshisha University, Masafumi NishidaAffiliated withFaculty of Science
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and Engineering, Doshisha University Buy this eBook * Final gross prices may vary according to local VAT. Get Access Abstract Computer Assisted Language Learning (CALL) systems are one of http://dl.acm.org/ft_gateway.cfm?ftid=1494182&id=2641527 the key technologies in assisting learners to master a second language. The progress in automatic speech recognition has advanced research on CALL systems that recognize speech constructed by students. Reliable recognition is still difficult from speech by second language speakers, which contains pronunciation, lexical, and grammatical errors. We developed a dialogue-based CALL system using a learner corpus. The system uses http://link.springer.com/chapter/10.1007%2F978-3-642-32790-2_75 two kinds of automatic speech recognizers using ngram and finite state automaton (FSA). We also propose a classification method for classifying the speech recognition results from the recognizer using FSA as accepted or rejected. The classification method uses the differences in acoustic likelihoods of both recognizers as well as the edit distance between strings of output words from both recognizers and coverage estimation by FSA over various expressions. Keywords CALL system learner corpus grammatical error Page %P Close Plain text Look Inside Chapter Metrics Provided by Bookmetrix Reference tools Export citation EndNote (.ENW) JabRef (.BIB) Mendeley (.BIB) Papers (.RIS) Zotero (.RIS) BibTeX (.BIB) Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn Supplementary Material (0) References (10) References1.Eskenazi, M.: Using Automatic Speech Processing for Foreign Language Pronunciation Tutoring: Some Issues and Prototype. Language Learning and Technology 2(2), 62–76 (1999)2.Kweon, O., Ito, A., Suzuki, M., Makino, S.: A grammatical error detection method for dialogue-based CALL system. J. Natural Language Processing 12(4), 137–156 (2005)CrossR
is a sentence that joins two independent clauses without punctuation or the appropriate conjunction. A comma splice is similar to a run-on sentence, but it uses a comma http://grammar.yourdictionary.com/grammar-rules-and-tips/5-most-common.html to join two clauses that have no appropriate conjunction.Fixing a run-on sentence or a comma splice can be accomplished in one of five different ways:Separate the clauses into two sentences.Replace the comma with a semi-colon.Replace the comma with a coordinating conjunction--and, but, for, yet, nor, so.Replace the comma with a subordinating conjunction--after, although, before, unless, as, because, even though, if, since, until, when, while.Replace the comma with grammatical error a semi-colon and transitional word--however, moreover, on the other hand, nevertheless, instead, also, therefore, consequently, otherwise, as a result.For example:Incorrect: Rachel is very smart, she began reading when she was three years old.Correct: Rachel is very smart. She began reading when she was three years old.Correct: Rachel is very smart; she began reading when she was three years old.Correct: Rachel is very smart, and she began reading grammatical error checker when she was three years old.Correct: Because Rachel is very smart, she began reading when she was three years old.Correct: Rachel is very smart; as a result, she began reading when she was three years old.Error #2: Pronoun ErrorsPronoun errors occur when pronouns do not agree in number with the nouns to which they refer. If the noun is singular, the pronoun must be singular. If the noun is plural, however, the pronoun must be plural as well. For example:Incorrect: Everybody must bring their own lunch.Correct: Everybody must bring his or her own lunch.Many people believe that pronoun errors are the result of writers who are trying to avoid the implication of sexist language. Although this is an admirable goal, correct grammar is still important.Error #3: Mistakes in Apostrophe UsageApostrophes are used to show possession. However, you do not use an apostrophe after a possessive pronoun such as my, mine, our, ours, his, hers, its, their, or theirs. For example:Incorrect: My mothers cabin is next to his' cabin.Correct: My mother's cabin is next to his cabin.In the case of it's, the apostrophe is used to indicate a contraction for it is. For example:Incorrect: Its a cold day in October.Correc