Authorship recognition via fluctuation analysis of network topology and word intermittency

Amancio, Diego R.

doi:10.1088/1742-5468/2015/03/p03005

Cited by 42 publications

(42 citation statements)

References 70 publications

(135 reference statements)

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“…This result is consistent with similar studies showing that the topology plays a relevant role in characterising complex systems, especially those conveying information [2,12,37,44]. Because the proposed representation is complementary to the traditional approaches, we advocate that the combination of features of distinct nature (traditional and topological) could lead to the improvement of similar tasks relying on the accurate characterisation of stylistic marks.…”

Section: Comparison With Traditional Methodssupporting

confidence: 89%

“…According to equation 3, the betweenness centrality can be interpreted as the network flow [39][40][41], which is a relevant quantity for the analysis of robustness of power-grid networks [42,43]. When applied to the analysis of text networks, this measurement has been interpreted as being useful to quantify the generality of words in which the word appears [44], which is in part motivated by the use of this measurement in community detection methods [45]. Unlike the clustering coefficient, the betweenness centrality uses the global connectivity information to quantify the specificity/generality of concepts [44].…”

Section: Complex Network Measurementsmentioning

confidence: 99%

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Using complex networks for text classification: Discriminating informative and imaginative documents

Arruda

Costa

Amancio

2016

EPL

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Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, which encompasses machine translation, automatic summarization and document classification. In the latter, many approaches have emphasized the semantical content of texts, as it is the case of bag-of-word language models. This approach has certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only on a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterizing texts.

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Section: Comparison With Traditional Methodssupporting

confidence: 89%

Section: Complex Network Measurementsmentioning

confidence: 99%

Using complex networks for text classification: Discriminating informative and imaginative documents

Arruda

Costa

Amancio

2016

EPL

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“…[With Suju Takano (1893-1976 and Seiho Awano (1899-1992) the four poets are referred to as "Four S of Little Cuckoo" or more simply "Four S, " due to the common initial in their pen names.] Finally, it should be mentioned that similarly to the artful fluctuation analysis of network topology and word intermittency [45] the present statistical method might be The distribution of sounds for the 203 arrangements is /u/: 30457, /o/: 55912, /a/: 59299, /e/: 11352, /i/: 42353, and /n/: 2779.…”

Section: Comparing Pattern Distributionsmentioning

confidence: 99%

Phonological Complexity in the Japanese Short Poetry: Coexistence Between Nearest-Neighbor Correlations and Far-Reaching Anticorrelations

Hayata

2018

Front. Phys.

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Sound correlations among six sites on the Japanese short poetry are analyzed in detail. For haiku composed of 5-7-5 syllables, the six sites are occupied by the head vowels on the three phrases as well as by the end sounds (in addition to five vowels, a syllabic nasal being included) on them. For tanka, first, one should divide the entire poem (5-7-5-7-7 syllables) into two parts, namely, the first (5-7-5) and the latter (5-7-7) part. For the six-dimensional array of sounds, there are 203 rhyme patterns possible. Specifically, for corpora of the haiku poetry, the complete works of three haiku poets, all of whom are distinguished in Japan, are selected. For those of the tanka, Collection of Ten Thousand Leaves and The Twenty-One Collections of Waka are chosen. Analyzed results through comparison between the surveyed and the expected frequencies show statistically the strong preference for the arrangements with a far-reaching antirhyming between sounds on the three ends, along with the correlation between those on the heads. In addition to the avoided rhyming, one finds a preference for specific arrangements exhibiting the nearest-neighbor correlations. Subsequently, to manifest the distinctive feature of each corpus, correlation analysis is carried out between frequency distributions of the arrangements. Moreover, in order to explore a statistical rule for the rank-ordered data, fitting is attempted to a long-tail distribution, followed by the Durbin-Watson testing. Quantitative analyses by using Gini's coefficient as well as Shannon's information entropy are also made. Finally, it is shown that the results of the complicated rhyming can be explained in part in comparison with the sound correlation between the first and the latter parts in the full names of Japanese.

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“…removed stop words from a text to work only with semantically meaningful words, which they further mapped to their singular and infinitive forms before linking adjacent words to form a word-to-word network. They then combined network features with linguistic features such as word frequency, intermittency, n -grams, and used machine learning techniques to identify the authors of various texts with reasonable accuracy [28, 29]. …”

Section: Complex-network Approaches To the Study Of Languagesmentioning

confidence: 99%

Functional shortcuts in language co-occurrence networks

2018

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Human language contains regular syntactic structures and grammatical patterns that should be detectable in their co-occurence networks. However, most standard complex network measures can hardly differentiate between co-occurence networks built from an empirical corpus and a body of scrambled text. In this work, we employ a motif extraction procedure to show that empirical networks have much greater motif densities. We demonstrate that motifs function as efficient and effective shortcuts in language networks, potentially explaining why we are able to generate and decipher language expressions so rapidly. Finally we suggest a link between motifs and constructions in Construction Grammar as well as speculate on the mechanisms behind the emergence of constructions in the early stages of language acquisition.

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Authorship recognition via fluctuation analysis of network topology and word intermittency

Cited by 42 publications

References 70 publications

Using complex networks for text classification: Discriminating informative and imaginative documents

Using complex networks for text classification: Discriminating informative and imaginative documents

Phonological Complexity in the Japanese Short Poetry: Coexistence Between Nearest-Neighbor Correlations and Far-Reaching Anticorrelations

Functional shortcuts in language co-occurrence networks

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