The complex networks approach for authorship attribution of books

Mehri, Ali; Darooneh, Amir H.; Shariati, Ashrafalsadat

doi:10.1016/j.physa.2011.12.011

Cited by 46 publications

(49 citation statements)

References 16 publications

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“…It therefore leads to a statement that information on individual language style used in a given text can be drawn from the set of characteristics describing the corresponding word-adjacency network. This is comparable with the known results concerning identification of individual language styles using the linguistic networks [1,30]; however, the previous research has typically focused on the network types and the network structure aspects other than considered here.…”

supporting

confidence: 87%

Linguistic data mining with complex networks: A stylometric-oriented approach

Stanisz

Kwapień

Dróżdż

2019

Information Sciences

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By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features.Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed. be simply derived or predicted solely from the properties of the system's individual elements. The existence of the emergent phenomena is often summarized with the phrases such as "more is different" or "the whole is something besides the parts".In this context, natural language is clearly a vivid instance of a complex system. Higher levels of its structure usually cannot be simply reduced to the sum of the elements involved. For example, phonemes or letters basically do not have any meaning, but the words consisting of them are references to specific objects and concepts. Likewise, knowing the meaning of separate words does not necessarily provide the understanding of a sentence composed of them, as a sentence can carry additional information, like an emotional load or a metaphorical message. Other features of natural language that are typical for complex systems have also been studied, for example long-range correlations [47], fractal and multifractal properties [5,10,13], self-organization [9,40] or lack of characteristic scale, which manifests itself in power laws such as the well-known Zipf's law or Heaps' law (the latter also referred to as the Herdan's law) [14,35,48].The network formalism has proven to be useful in studying and processing natural language. It allows to represent language on many levels of its structure -the linguistic networks can be constructed to reflect word co-occurrence, semantic similarity, grammatical relationships etc. It has a significant practical importance -the methods of analysis of graphs and networks have been employed in the natural language processing tasks, like keyword selection, document summarization, word-sense disambig...

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supporting

confidence: 87%

Linguistic data mining with complex networks: A stylometric-oriented approach

Stanisz

Kwapień

Dróżdż

2019

Information Sciences

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“…Comparing our results with others that use co-occurrence networks, Amancio et al [15] obtained an accuracy rate of 65%. From the dataset used by Mehri, Darooneh and Shariati [16], 28 books (77.7%) were correctly classified. The results retrieved in this paper may be seen as complementary measurements and can be combined with traditional techniques usually employed in authorship attribution.…”

Section: Discussionmentioning

confidence: 99%

“…In this type of network, links are established by connecting adjacent words, since most of the syntactical relations occur among neighbouring words [14]. The representation of texts as word co-occurrence networks has proven useful to tackle different tasks, for example, to create automatic extractive summarizers [9] and to identify the authorship of books [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Authorship Attribution via Network Motifs Identification

Marinho

Hirst

Amancio

2016

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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Abstract-Concepts and methods of complex networks can be used to analyse texts at their different complexity levels. Examples of natural language processing (NLP) tasks studied via topological analysis of networks are keyword identification, automatic extractive summarization and authorship attribution. Even though a myriad of network measurements have been applied to study the authorship attribution problem, the use of motifs for text analysis has been restricted to a few works. The goal of this paper is to apply the concept of motifs, recurrent interconnection patterns, in the authorship attribution task. The absolute frequencies of all thirteen directed motifs with three nodes were extracted from the co-occurrence networks and used as classification features. The effectiveness of these features was verified with four machine learning methods. The results show that motifs are able to distinguish the writing style of different authors. In our best scenario, 57.5% of the books were correctly classified. The chance baseline for this problem is 12.5%. In addition, we have found that function words play an important role in these recurrent patterns. Taken together, our findings suggest that motifs should be further explored in other related linguistic tasks.

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“…A well-known model for text representation is the so-called word adjacency (cooccurrence) model, where two words are linked if they appear as neighbors in the text. Though seemingly simple, this model is able to capture authors' styles [19][20][21], textual complexity [8,22] and many other textual aspects [18,23,24]. A similar model, referred to as syntactical network, takes into account the syntactical rep-resentation of texts by connecting words syntactically related.…”

Section: Introductionmentioning

confidence: 99%

Topic segmentation via community detection in complex networks

Arruda

Costa

Amancio

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Many real systems have been modelled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting findings, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such representation fails in capturing other textual features, such as the organization in topics or subjects. In this context, we propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favours the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We have found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study semantical features of texts.

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The complex networks approach for authorship attribution of books

Cited by 46 publications

References 16 publications

Linguistic data mining with complex networks: A stylometric-oriented approach

Linguistic data mining with complex networks: A stylometric-oriented approach

Authorship Attribution via Network Motifs Identification

Topic segmentation via community detection in complex networks

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