An NLP Based Plagiarism Detection Approach for Short Sentences
Shikha Pandey1, Arpana Rawal2

1Shikha Pandey, Department of CSE, Chhattisgarh Swami Vivekanand Technical University, Bhilai, (Chhattisgarh), India.
2Dr. Arpana Rawal, Department of CSE, Chhattisgarh Swami Vivekanand Technical University, Bhilai, (Chhattisgarh), India.

Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 215-219 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1831017519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The notable issue in the fields of plagiarism detection is, to assess the semantic similarity between obfuscated sentences, and it becomes more completed in case of short sentences (only 4-8 words). An innovative approach, typed dependencies relationship (TDR), based on Natural Language processing is presented for detecting plagiarism on short sentences. In this study proposed approach performed on previous datasets of short sentences and compared results with 3 state-of-art methods. The investigation shows that the proposed calculation has exceptional execution in taking care of sentences with complex linguistic structure.
Keywords: Type Dependencies Relationship, Plagiarism Detection, Sentence Similarity, Syntactic and Semantic Similarity

Scope of the Article: Reflection and Metadata Approaches.