Plagirism checker

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  plagiarism is when you pass off other people's text as your own
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  • 1. Plagiarism Checker
  • 2. What is Plagiarism ? to steal and pass off (the ideas or words of another) as one's own to use (another's production) without crediting the source to commit literary theft to present as new and original an idea or product derived from an existing source Not just Copying or borrowing
  • 3. Types of Plagiarism ? CLONE Submitting another’s work, word-for-word, as one’s own CTRL-C Contains significant portions of text from a single source without alterations FIND - REPLACE Changing key words and phrases but retaining the essential content of the source REMIX Paraphrases from multiple sources, made to fit together RECYCLE Borrows generously from the writer’s previous work without citation HYBRID Combines perfectly cited sources with copied passages without citation MASHUP Mixes copied material from multiple sources 404 ERROR Includes citations to non-existent or inaccurate information about AGGREGATOR Includes proper citation to sources but the paper contains almost no RE-TWEET Includes proper citation, but relies too closely on the text’s original wording
  • 4. Algorithm
  • 5. How To do it practically Document 1 • A document is a written, drawn, presented or recorded representation of thoughts. Originating from the Latin Documentum meaning lesson - the verb doceō means to teach, and is pronounced similarly, in the past it was usually used as a term for a written proof used as evidence. In the computer age, a document is usually used to describe a primarily textual file, along with its structure and design, such as fonts, colors and additional images. Document 2 • A document is a written, drawn, presented or recorded representation of thoughts. Originating from the Latin Documentum meaning lesson - the verb doceō means to teach, and is pronounced similarly, in the past it was usually used as a term for a written proof used as evidence. In the computer age, a document is usually used to describe a primarily textual file, along with its structure and design, such as fonts, colors and additional images. Threeshold
  • 6. Algorithm 1 (document level), Algorithm 3 (sentence level). (Lexical semantics )-lesk WordNet Algorithm 2 (paragraph level),
  • 7. Two input documents • Input : DocA, DocB // Two input documents • Output: similarity • Begin • DocMinSize = min (|DocA|, |DocB|) • DocIntersectionSize = |DocA ∩ DocB| • If (DocIntersectionSize >= DocMinSize*DocThreshold) • Then • //Possible similarity • //Check similarity at paragraph level • similarity = true • Else • similarity = false • End
  • 8. Two input paragraphs • Input : ParA, ParB // Two input paragraphs Output: similarity • Begin • ParMinSize = min (|ParA|, |ParB|) • ParIntersectionSize = |ParA ∩ ParB| • If (ParIntersectionSize >= ParMinSize*ParThreshold) • Then • //Possible similarity • //Check similarity at sentence level • similarity = true • Else • similarity = false
  • 9. Sentence level • Algorithm 3: Sentence level heuristic • Input : SenA, SenB • Output: similarity, similar substrings in SenA and SenB • Begin • SenMinSize = min(|SenA|, |SenB|) • SenIntersectionSize = |SenA ∩ SenB| • If (SenIntersectionSize >= SenMinSize*SenThreshold) • Then • //Similarity detected • //Determine similar • //substrings • similarity = true • Else • similarity = false • Else • similarity = false • End
  • 10. Wordnet WordNet •A very large lexical database of English: –117K nouns, 11K verbs, 22K adjectives, 4.5K adverbs •Word senses grouped into synonym sets (“synsets”) linked into a conceptual-semantic hierarchy –82K noun synsets, 13K verb synsets, 18K adjectives synsets, 3.6K adverb synsets –Avg. # of senses: 1.23/noun, 2.16/verb, 1.41/adj, 1.24/adverb •Conceptual-semantic relations –hypernym/hyponym
  • 11. Lesk algorithm Compare the context with the dictionary definition of the sense –Construct the signatureof a word in context by the signatures of its senses in the dictionary •Signature= set of context words (in examples/gloss or in context) –Assign the dictionary sense whose gloss and examples are the most similarto the context in which the word occurs •Similarity = size of intersection of context signature and sense signature
  • 12. Sense signatures -------bank1 Gloss: a financial institution that accepts deposits and channels the moneyinto lending activities Examples: “he cashedthe checkat the bank”, “that bank holdsthe mortgageon my home” ------bank2 Gloss: slopingland(especially the slopebeside a bodyof water) Examples: “they pulledthe canoeup on the bank”, “he saton the bank of the riverand watchedthe current” Signature(bank1) = {financial, institution, accept, deposit, channel, money, lend, activity, cash, check, hold, mortgage, home} Signature(bank1) = {slope, land, body, water, pull, canoe, sit, river, watch, current}
  • 13. Final Result Uniqe Also may be containing a report with details
  • 14. Team Members NLP Eslam Hamouda Ahmed Wahdan Hossam Nabih Mohamed Shalan
  • 15. Demo
  • 16. Thank You
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