Search Results for author:
Found 35 papers, 1 papers with code
Machine learning and economic forecasting: the role of international trade networks
This study examines the effects of de-globalization trends on international trade networks and their role in improving forecasts for economic growth.
Using Full-Text Content to Characterize and Identify Best Seller Books
Such an outcome suggests that it is unfeasible to predict the success of books with high accuracy using only the full content of the texts.
Recovering network topology and dynamics via sequence characterization
Given that in many real scenarios, the underlying processes generating the sequence is hidden, we investigate whether the reconstruction of the network via the co-occurrence method is useful to recover both the network topology and agent dynamics generating sequences.
Using virtual edges to extract keywords from texts modeled as complex networks
Detecting keywords in texts is important for many text mining applications.
Text characterization based on recurrence networks
In particular, texts are also characterized by a hierarchical structure that can be approached by using multi-scale concepts and methods.
A pattern recognition approach for distinguishing between prose and poetry
Interestingly, by using an approach based on complex networks to visualize the similarities between the different texts considered, we found that the patterns of poetry vary much more than prose.
On predicting research grants productivity
We found that research subject and publication history play a role in predicting productivity.
Language Networks: a Practical Approach
This manuscript provides a short and practical introduction to the topic of language networks.
Using word embeddings to improve the discriminability of co-occurrence text networks
Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios.
Semantic flow in language networks
In this study we propose a framework to characterize documents based on their semantic flow.
Paragraph-based complex networks: application to document classification and authenticity verification
With the increasing number of texts made available on the Internet, many applications have relied on text mining tools to tackle a diversity of problems.
Word sense induction using word embeddings and community detection in complex networks
In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word.
Extractive Multi-document Summarization Using Multilayer Networks
Differently from previous studies in multi-document summarization, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer).
An Image Analysis Approach to the Calligraphy of Books
Text network analysis has received increasing attention as a consequence of its wide range of applications.
Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks
Due to the large amount of textual information available on Internet, it is of paramount relevance to use techniques that find relevant and concise content.
On the "Calligraphy" of Books
In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative.
On the role of words in the network structure of texts: application to authorship attribution
Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e. g. letters, words and bigrams, while methods based on co-occurrence networks consider the structure of texts regardless of the nodes label (i. e. the words semantics).
Labelled network subgraphs reveal stylistic subtleties in written texts
The vast amount of data and increase of computational capacity have allowed the analysis of texts from several perspectives, including the representation of texts as complex networks.
Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts
The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised.
Clustering Algorithms: A Comparative Approach
We also found that the default configuration of the adopted implementations was not accurate.
Authorship Attribution Based on Life-Like Network Automata
Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works.
Text authorship identified using the dynamics of word co-occurrence networks
The identification of authorship in disputed documents still requires human expertise, which is now unfeasible for many tasks owing to the large volumes of text and authors in practical applications.
Representation of texts as complex networks: a mesoscopic approach
In order to grasp the mesoscopic characteristics of semantical content in written texts, we devised a network model which is able to analyze documents in a multi-scale fashion.
Word sense disambiguation: a complex network approach
Our results revealed that the proposed learning algorithm in such bipartite networks provides excellent results mostly when topical features are employed to characterize the context.
Complex systems: features, similarity and connectivity
More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas.
Topic segmentation via community detection in complex networks
Many real systems have been modelled in terms of network concepts, and written texts are a particular example of information networks.
Network analysis of named entity co-occurrences in written texts
The use of methods borrowed from statistics and physics to analyze written texts has allowed the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure.
Classifying informative and imaginative prose using complex networks
In the latter, many approaches have emphasized the semantical content of texts, as it is the case of bag-of-word language models.
A complex network approach to stylometry
Statistical methods have been widely employed to study the fundamental properties of language.
Comparing the writing style of real and artificial papers
Recent years have witnessed the increase of competition in science.
Concentric network symmetry grasps authors' styles in word adjacency networks
A statistical analysis of the symmetry distribution performed in several novels showed that most of the words do not display symmetric patterns of connectivity.
Authorship recognition via fluctuation analysis of network topology and word intermittency
Because fluctuations represent a pivotal factor for characterizing a myriad of real systems, this study focused on the analysis of the properties of stylistic fluctuations in texts via topological analysis of complex networks and intermittency measurements.
Probing the topological properties of complex networks modeling short written texts
Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks.
A perspective on the advancement of natural language processing tasks via topological analysis of complex networks
Comment on "Approaching human language with complex networks" by Cong and Liu (Physics of Life Reviews, Volume 11, Issue 4, December 2014, Pages 598-618).
Discriminating word senses with tourist walks in complex networks
Patterns of topological arrangement are widely used for both animal and human brains in the learning process.
