| Full text | |
| Author(s): |
Marinho, Vanessa Queiroz
;
Hirst, Graeme
;
Amancio, Diego Raphael
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF 2016 5TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2016); v. N/A, p. 6-pg., 2016-01-01. |
| 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, i.e. 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. (AU) | |
| FAPESP's process: | 15/05676-8 - Development of new models for authorship recognition using complex networks |
| Grantee: | Vanessa Queiroz Marinho |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 14/20830-0 - Using complex networks to recognize patterns in written texts |
| Grantee: | Diego Raphael Amancio |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 15/23803-7 - Authorship attribution with traditional methods and complex networks |
| Grantee: | Vanessa Queiroz Marinho |
| Support Opportunities: | Scholarships abroad - Research Internship - Master's degree |