Use of text syntactical structures in detection of document duplicates
Abstract
This is the first paper on a set of experiments addressing issues related to the determination of text similarity using a combined syntactical representation and string alignment techniques. The suggested approach takes advantage of document syntactical structure manifested in Part of Speech (POS) tags and uses it as a basis for further processing. Documents, including the query, are pre-processed using a POS tagger converting them into a reduced string that captures some of the writing style of authors and some of the semantics of the written text. This provides means of representing a document on a higher level of abstraction that captures the different alterations that can be done on documents that are similar in origin and possibly in style. This in turn enables processing of such documents using many of the available string manipulation and matching algorithms. This work is inspired and driven by the parallel between text processing and sequence alignment in computational biology. Sequence alignment techniques are used to analyze and establish the utility of using strings produced as a result of this syntactical representation of content.
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SYN methods are based on decomposing documents into statements and extracting POS features.
“SYN methods [6], [12] are based on decomposing documents into statements and extracting POS features.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [12]
In contrast to many existing plagiarism detection systems that reduce the text into a set of tokens by removing stop words and stemming, the approach in [6], [12], and [13] reduces the text into a smaller set of syntactical tags, thus making use of most of the content.
“In contrast to many existing plagiarism detection systems that reduce the text into a set of tokens by removing stop words and stemming, the approach in [6], [12], and [13] reduces the text into a smaller set of syntactical tags, thus making use of most of the content.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [12]
Cites
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · see citing works
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Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299 · see citing works
- Authors
- Elhadi, M., & Al-Tobi, A.
- Affiliations
- Sultan Qaboos University (OM)
- ORCID
- Elhadi, Mohamed
- Venue
- 2008 Third International Conference on Digital Information Management
- Publisher
- IEEE
- Published
- 2008-11
- Pages
- 520-525
- OpenAlex
- W2113937265
- Cited by
- 36
- References
- 1
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 3 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 4.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Digital Information Management (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- text similarity, part-of-speech tagging, syntactic representation, string alignment, sequence alignment, author style, document representation, information retrieval
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