Name
Abstract | ||
|---|---|---|
In this paper we compare the robustness of several types of stylistic markers to help discriminate authorship at sentence level. We train a SVM-based classifier using each set of features separately and perform sentence-level authorship analysis over corpus of editorials published in a Portuguese quality newspaper. Results show that features based on POS information, punctuation and word / sentence length contribute to a more robust sentence-level authorship analysis. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12320-7_7 | PROPOR |
Keywords | Field | DocType |
stylistic marker,svm-based classifier,portuguese quality newspaper,sentence-level authorship analysis,sentence level,robust sentence-level authorship analysis,sentence-level feature,pos information,sentence length,results show,discriminate authorship | Computer science,Support vector machine,Portuguese,Newspaper,Speech recognition,Robustness (computer science),Natural language processing,Artificial intelligence,Classifier (linguistics),Sentence,Discriminant function analysis,Punctuation | Conference |
Volume | ISSN | ISBN |
6001 | 0302-9743 | 3-642-12319-8 |
Citations | PageRank | References |
2 | 0.45 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Sousa-Silva | 1 | 2 | 0.45 |
| Luís Sarmento | 2 | 377 | 31.16 |
| Tim Grant | 3 | 23 | 1.58 |
| Eugénio Oliveira | 4 | 974 | 111.00 |
| Belinda Maia | 5 | 24 | 2.75 |