Authorship Attribution using Variable Length Part-of-Speech Patterns

Pokou, Yao Jean Marc; Fournier‐Viger, Philippe; Moghrabi, Chadia

doi:10.5220/0005710103540361

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…To give the reader a feel of the relative efficacy of this approach on traditional long texts, we present a summary table of results on a corpus of 30 books by ten 19th century authors (c.f. Table 11 for comparative results) [18,19]. In future work, we plan to develop other features and also evaluate the proposed features on other types of texts.…”

Section: Discussionmentioning

confidence: 99%

Using Frequent Fixed or Variable-Length POS Ngrams or Skip-Grams for Blog Authorship Attribution

Pokou

Fournier-Viger

Moghrabi

2016

IFIP Advances in Information and Communication Technology

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Authorship attribution is the process of identifying the author of an unknown text from a finite set of known candidates. In recent years, it has become increasingly relevant in social networks, blogs, emails and forums where anonymous posts, bullying, and even threats are sometimes perpetrated. State-of-the-art systems for authorship attribution often combine a wide range of features to achieve high accuracy. Although many features have been proposed, it remains an important challenge to find new features and methods that can characterize each author and that can be used on non formal or short writings like blog content or emails. In this paper, we present a novel method for authorship attribution using frequent fixed or variable-length part-of-speech patterns (ngrams or skip-grams) as features to represent each author's style. This method allows the system to automatically choose its most appropriate features as those sequences being used most frequently. An experimental evaluation on a collection of blog posts shows that the proposed approach is effective at discriminating between blog authors.

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Section: Discussionmentioning

confidence: 99%

Using Frequent Fixed or Variable-Length POS Ngrams or Skip-Grams for Blog Authorship Attribution

Pokou

Fournier-Viger

Moghrabi

2016

IFIP Advances in Information and Communication Technology

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“…Implementing SABA starts from uploading the chosen novels in text mode (with .txt extension). The chunking step is performed to select the attribute set type, whether it is a special character, punctuation symbol, or statement length in words (the number of words counted between any punctuation symbol and special character) with pre-and post-traditional cleansing [13]. Steps 1-5 in the proposed method are feature selection steps that are utilized for machine learning data training, whereas step 6 represents feature extraction for data testing.…”

Section: Saba Methods Procedures Using Non-word Attributesmentioning

confidence: 99%

“…The dataset source was introduced first by Zhao and Zobel in 2006 [15]. It was then utilized by Jamil and Mustafa in 2018 [4], Kim in 2015 [6], Pokou et al in 2016 [13], and Mustafa et al with their project team for Stylometric research in 2010 [12]. The dataset is a subset of the 19th century English literature.…”

Section: The Datasetmentioning

confidence: 99%

Non-word Attributes’ Efficiency in Text Mining Authorship Prediction

Mustafa

2019

Journal of Intelligent Systems

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Abstract Literature scripts can be compared to paintings, in an artistic way as well as in the perspective of financial value, whereas the value of these scripts rise and fall depending on their author’s popularity. Authors’ scripts represent a specific style of writing that can be measured and compared using a text mining field called Stylometric. Stylometric analysis depends on some features called authorship attributes, and these attributes or features can be used in special algorithms and methods to reach that aim. Generally, each method selected in the Stylometric field uses a variety of attributes to reach higher prediction accuracy. The aim of this research is to improve the accuracy of authorship prediction in literary works based on the artistic writing style of the authors. To achieve that, a new set of attributes will be used with the Stylometric Authorship Balanced Attribution method, which was chosen in this research among several other machine language methods because of its delicateness in authorship prediction projects. The attributes that have been used by most of the researchers were word frequencies (single word, pair of words, or trio of words), which led to some prediction mistakes. In this research, a new set of attributes is used to decrease these mistakes. These proposed non-word attributes are named sentence length, special characters, and punctuation symbols. The results obtained by using these proposed attributes were excellent.

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“…In Wei et al 48 and Sabrina et al 49 All the mentioned algorithms are examples of SPM, which has numerous applications in fields including bioinformatics (see Wang, 2007), 51 market basket analysis (see Srikant, 1996), 52 and text analysis (see Pokou, 2016). 53 This study compares the proposed SPARSS algorithm with the GSP, DLA, and PrefixSpan methods.…”

Section: Parallelize the Spade Algorithm With The Recursive Dynamic Loadmentioning

confidence: 99%

On the big data processing algorithms for finding frequent sequences

Can¹,

Zaval

Uzun‐Per

et al. 2023

Concurrency and Computation

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Sequential pattern mining algorithms extract trendy sequence appearances inside ordered transactional datasets such as market basket datasets. There is a lack of research employing big data processing techniques to locate frequent sequences on large‐scale datasets. Furthermore, there is a need for optimized sequential pattern mining algorithms that run on ordered one‐dimensional sequences. We also observe a lack of sequential pattern search studies in the literature, where the focus is centered around multi‐dimensional data sequences. Existing approaches that deal with ordered one‐dimensional datasets suffer from scalability issues as the amount of data to be analyzed is enormous. This research investigates the big data processing techniques used to find frequent sequences in large‐scale datasets. It also proposes a scalable sequence pattern mining algorithm called Sequential Pattern Acquisition by Reducing Search Space (SPARSS) designed for distributed data processing systems that efficiently handle large datasets containing sequential one‐element data. It introduces a prototype implementation of SPARSS and provides information on the SPARSS's memory and time requirements, which were calculated as part of experimental studies on a real‐world dataset. The results confirm our expectations and demonstrate SPARSS's superior scalability and run‐time efficiency compared to other distributed algorithms.

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Authorship Attribution using Variable Length Part-of-Speech Patterns

Cited by 18 publications

References 18 publications

Using Frequent Fixed or Variable-Length POS Ngrams or Skip-Grams for Blog Authorship Attribution

Using Frequent Fixed or Variable-Length POS Ngrams or Skip-Grams for Blog Authorship Attribution

Non-word Attributes’ Efficiency in Text Mining Authorship Prediction

On the big data processing algorithms for finding frequent sequences

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