| dc.contributor.author | Agun, Hayri Volkan | |
| dc.contributor.author | Yılmazel, Özgür | |
| dc.date.accessioned | 2019-10-21T19:44:08Z | |
| dc.date.available | 2019-10-21T19:44:08Z | |
| dc.date.issued | 2017 | |
| dc.identifier.isbn | 978-1-5386-2150-9 | |
| dc.identifier.uri | https://hdl.handle.net/11421/19814 | |
| dc.description | 2nd International Conference on Knowledge Engineering and Applications (ICKEA) -- OCT 21-23, 2017 -- Imperial Coll, London, ENGLAND | en_US |
| dc.description | WOS: 000428208400037 | en_US |
| dc.description.abstract | Authorship attribution has been well studied in terms of text classification with many diverse feature sets. However, finding topic independent features is hard and trained models with hand crafted features in one domain may not work in another domain. In this study we used a semi supervised neural language model which is known as document embeddings for authorship attribution problem. This method showed significant improvements over bag-of-words representations in a well-known dataset. | en_US |
| dc.description.sponsorship | IEEE | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Authorship Attribution | en_US |
| dc.subject | Document Embeddings | en_US |
| dc.subject | Bag Of Words Model | en_US |
| dc.subject | Text Classification | en_US |
| dc.title | Document Embedding Approach for Efficient Authorship Attribution | en_US |
| dc.type | conferenceObject | en_US |
| dc.relation.journal | Proceedings of 2017 2Nd International Conference On Knowledge Engineering and Applications (Ickea) | en_US |
| dc.contributor.department | Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.identifier.startpage | 194 | en_US |
| dc.identifier.endpage | 198 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US] |
| dc.contributor.institutionauthor | Yılmazel, Özgür | |
