Labelled network subgraphs reveal stylistic subtleties in written texts

Marinho, Vanessa Queiroz; Hirst, Graeme; Amancio, Diego R.

doi:10.1093/comnet/cnx047

Cited by 10 publications

(6 citation statements)

References 52 publications

(74 reference statements)

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“…A different approach to analyze co-occurrence networks was devised in [36]. Whilst most approaches only considered traditional network measurements or devised novel topological and dynamical measurements, the authors combined networked and semantic information to improve the performance of network-based classification.…”

Section: Related Workmentioning

confidence: 99%

Using word embeddings to improve the discriminability of co-occurrence text networks

Quispe,

Tohalino,

Amancio

2020

Preprint

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Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text.Here we investigate whether the use of word embeddings as a tool to create virtual links in cooccurrence networks may improve the quality of classification systems. Our results revealed that the discriminability in the stylometry task is improved when using Glove, Word2Vec and FastText.In addition, we found that optimized results are obtained when stopwords are not disregarded and a simple global thresholding strategy is used to establish virtual links. Because the proposed approach is able to improve the representation of texts as complex networks, we believe that it could be extended to study other natural language processing tasks. Likewise, theoretical languages studies could benefit from the adopted enriched representation of word co-occurrence networks.

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Section: Related Workmentioning

confidence: 99%

Using word embeddings to improve the discriminability of co-occurrence text networks

Quispe,

Tohalino,

Amancio

2020

Preprint

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“…More recently, an approach based on labelled motifs showed that authors tend to use words in combination with particular motifs [49].…”

Section: Motifsmentioning

confidence: 99%

Semantic flow in language networks

Corrêa,

Marinho,

Amancio

2019

Preprint

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In this study we propose a framework to characterize documents based on their semantic flow. The proposed framework encompasses a network-based model that connected sentences based on their semantic similarity. Semantic fields are detected using standard community detection methods. as the story unfolds, transitions between semantic fields are represent in Markov networks, which in turned are characterized via network motifs (subgraphs). Here we show that the proposed framework can be used to classify books according to their style and publication dates. Remarkably, even without a systematic optimization of parameters, philosophy and investigative books were discriminated with an accuracy rate of 92.5%.Because this model captures semantic features of texts, it could be used as an additional feature in traditional network-based models of texts that capture only syntactical/stylistic information, as it is the case of word adjacency (co-occurrence) networks.

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“…A similar idea could also be applied to the analysis of other stylometric tasks. In addition, we suggest that the semantics of the texts could be combined with the concept of semantic flow by using ''labeled motifs'', as proposed in our previous work [59]. Since semantic networks have been studied in cognitive sciences, we believe that the adopted network representation could be adapted and used -as an auxiliary tool -to study complex brain and cognitive processes that could assist the diagnosis of cognitive disorders via text analysis [73,75].…”

Section: Resultsmentioning

confidence: 99%

“…For example, in the authorship recognition task, patterns extracted from a semantic flow analysis could be combined with other techniques to improve the characterization of authors [73,74]. In fact, the use of motifs in the microscopic level has already provided a good characterization of authors [59]. A similar idea could also be applied to the analysis of other stylometric tasks.…”

Section: Resultsmentioning

confidence: 99%

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Using complex networks and Deep Learning to model and learn context

Júnior¹

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The structure of language is strongly influenced by the context, whether it is the social setting, of discourse (spoken and written) or the context of words itself. This fact allowed the creation of several techniques of Natural Language Processing (NLP) that take advantage of this information to tackle a myriad of tasks, including machine translation, summarization and classification of texts. However, in most of these applications, the context has been approached only as a source of information and not as an element to be explored and modeled. In this thesis, we explore the context on a deeper level, bringing new representations and methodologies. Throughout the thesis, we considered context as an important element that must be modeled in order to better perform NLP tasks. We demonstrated how complex networks can be used both to represent and learn context information while performing word sense disambiguation. In addition, we proposed a context modeling approach that combines word embeddings and a network representation, this approach allowed the induction of senses in an unsupervised way using community detection methods. Using this representation we further explored its application in text classification, we expanded the approach to allow the extraction of text features based on the semantic flow, which were later used in a supervised classifier trained to discriminate texts by genre and publication date. The studies carried out in this thesis demonstrate that context modeling is important given the interdependence between language and context, and that it can bring benefits for different NLP tasks. The framework proposed, both for modeling and textual feature extraction can be further used to explore other aspects and mechanisms of language.

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Labelled network subgraphs reveal stylistic subtleties in written texts

Cited by 10 publications

References 52 publications

Using word embeddings to improve the discriminability of co-occurrence text networks

Using word embeddings to improve the discriminability of co-occurrence text networks

Semantic flow in language networks

Using complex networks and Deep Learning to model and learn context

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