Spelling bee quiz ppt

spelling of powerpoint and spelling powerpoint presentations
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Dr.DouglasPatton,United States,Teacher
Published Date:26-07-2017
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Spelling Correction and the Noisy Channel The Spelling Correction TaskDan Jurafsky Applications for spelling correction Word processing Phones Web search 2Dan Jurafsky Spelling Tasks • Spelling Error Detection • Spelling Error Correction: • Autocorrect • htethe • Suggest a correction • Suggestion lists 3Dan Jurafsky Types of spelling errors • Non-word Errors • graffegiraffe • Real-word Errors • Typographical errors • threethere • Cognitive Errors (homophones) • piecepeace, • too two 4Dan Jurafsky Rates of spelling errors 26%: Web queries Wang et al. 2003 13%: Retyping, no backspace: Whitelaw et al. English&German 7%: Words corrected retyping on phone-sized organizer 2%: Words uncorrected on organizer Soukoreff &MacKenzie 2003 1-2%: Retyping: Kane and Wobbrock 2007, Gruden et al. 1983 5Dan Jurafsky Non-word spelling errors • Non-word spelling error detection: • Any word not in a dictionary is an error • The larger the dictionary the better • Non-word spelling error correction: • Generate candidates: real words that are similar to error • Choose the one which is best: • Shortest weighted edit distance • Highest noisy channel probability 6Dan Jurafsky Real word spelling errors • For each word w, generate candidate set: • Find candidate words with similar pronunciations • Find candidate words with similar spelling • Include w in candidate set • Choose best candidate • Noisy Channel • Classifier 7Spelling Correction and the Noisy Channel The Spelling Correction TaskSpelling Correction and the Noisy Channel The Noisy Channel Model of SpellingDan Jurafsky Noisy Channel Intuition 10Dan Jurafsky Noisy Channel • We see an observation x of a misspelled word • Find the correct word w ˆ w =argmaxP(wx) wÎV P(xw)P(w) =argmax wÎV P(x) =argmaxP(xw)P(w) wÎV 11Dan Jurafsky History: Noisy channel for spelling proposed around 1990 • IBM • Mays, Eric, Fred J. Damerau and Robert L. Mercer. 1991. Context based spelling correction. Information Processing and Management, 23(5), 517–522 • AT&T Bell Labs • Kernighan, Mark D., Kenneth W. Church, and William A. Gale. 1990. A spelling correction program based on a noisy channel model. Proceedings of COLING 1990, 205-210Dan Jurafsky Non-word spelling error example acress 13Dan Jurafsky Candidate generation • Words with similar spelling • Small edit distance to error • Words with similar pronunciation • Small edit distance of pronunciation to error 14Dan Jurafsky Damerau-Levenshtein edit distance • Minimal edit distance between two strings, where edits are: • Insertion • Deletion • Substitution • Transposition of two adjacent letters 15Dan Jurafsky Words within 1 of acress Error Candidate Correct Error Type Correction Letter Letter acress actress t - deletion acress cress - a insertion transposition acress caress ca ac substitution acress access c r substitution acress across o e acress acres - s insertion insertion acress acres - s 16Dan Jurafsky Candidate generation • 80% of errors are within edit distance 1 • Almost all errors within edit distance 2 • Also allow insertion of space or hyphen • thisidea this idea • inlaw in-law 17Dan Jurafsky Language Model • Use any of the language modeling algorithms we’ve learned • Unigram, bigram, trigram • Web-scale spelling correction • Stupid backoff 18Dan Jurafsky Unigram Prior probability Counts from 404,253,213 words in Corpus of Contemporary English (COCA) word Frequency of word P(word) actress 9,321 .0000230573 cress 220 .0000005442 caress 686 .0000016969 access 37,038 .0000916207 across 120,844 .0002989314 acres 12,874 .0000318463 19Dan Jurafsky Channel model probability • Error model probability, Edit probability • Kernighan, Church, Gale 1990 • Misspelled word x = x , x , x … x 1 2 3 m • Correct word w = w , w , w ,…, w 1 2 3 n • P(xw) = probability of the edit • (deletion/insertion/substitution/transposition) 20

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