Sentence based topic modeling
| buir.advisor | Ulusoy, Özgür | |
| dc.contributor.author | Sarı, Can Taylan | |
| dc.date.accessioned | 2016-07-01T11:10:22Z | |
| dc.date.available | 2016-07-01T11:10:22Z | |
| dc.date.issued | 2014 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description.abstract | Fast augmentation of large text collections in digital world makes inevitable to automatically extract short descriptions of those texts. Even if a lot of studies have been done on detecting hidden topics in text corpora, almost all models follow the bag-of-words assumption. This study presents a new unsupervised learning method that reveals topics in a text corpora and the topic distribution of each text in the corpora. The texts in the corpora are described by a generative graphical model, in which each sentence is generated by a single topic and the topics of consecutive sentences follow a hidden Markov chain. In contrast to bagof-words paradigm, the model assumes each sentence as a unit block and builds on a memory of topics slowly changing in a meaningful way as the text flows. The results are evaluated both qualitatively by examining topic keywords from particular text collections and quantitatively by means of perplexity, a measure of generalization of the model. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | Sarı, Can Taylan | en_US |
| dc.format.extent | ix, 67 leaves, tables, graphics | en_US |
| dc.identifier.itemid | B138031 | |
| dc.identifier.uri | http://hdl.handle.net/11693/30000 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | probabilistic graphical model | en_US |
| dc.subject | topic model | en_US |
| dc.subject | hidden Markov model | en_US |
| dc.subject | Markov chain Monte Carlo | en_US |
| dc.subject.lcc | QA279 .S27 2014 | en_US |
| dc.subject.lcsh | Graphical modeling (Statistics) | en_US |
| dc.title | Sentence based topic modeling | en_US |
| dc.type | Thesis | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- 0006635.pdf
- Size:
- 748.85 KB
- Format:
- Adobe Portable Document Format
- Description:
- Full printable version