BOUGHACI et al., 2019 - Google Patents
An improved N-grams based Model for Authorship AttributionBOUGHACI et al., 2019
- Document ID
- 16879941956967348634
- Author
- BOUGHACI D
- BENMESBAH M
- ZEBIRI A
- Publication year
- Publication venue
- 2019 International Conference on Computer and Information Sciences (ICCIS)
External Links
Snippet
Authorship attribution is the problem of studying an anonymous text and finding the corresponding author in a set of candidate authors. In this paper, we propose a method based on N-grams model for the problem of authorship attribution. Several measures are …
- 230000000875 corresponding 0 abstract description 10
Classifications
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- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
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- G06F17/2765—Recognition
- G06F17/277—Lexical analysis, e.g. tokenisation, collocates
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- G06—COMPUTING; CALCULATING; COUNTING
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- G06F17/30716—Browsing or visualization
- G06F17/30719—Summarization for human users
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G—PHYSICS
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- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
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- G—PHYSICS
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- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
- G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
- G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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