Semi-supervised learning based named entity recognition for morphologically rich languages
| dc.contributor | Graduate Program in Computer Engineering. | |
| dc.contributor.advisor | Özgür, Arzucan. | |
| dc.contributor.author | Demir, Hakan. | |
| dc.date.accessioned | 2023-03-16T10:01:56Z | |
| dc.date.available | 2023-03-16T10:01:56Z | |
| dc.date.issued | 2014. | |
| dc.description.abstract | In this study, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly in ectional Turkish and Czech languages and obtained better F-score performances than the previously published results for these languages. We improved the state-of-the-art F-score by 2.26% for Turkish and 1.53% for Czech. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages. | |
| dc.format.extent | 30 cm. | |
| dc.format.pages | xi, 40 leaves ; | |
| dc.identifier.other | CMPE 2014 D46 | |
| dc.identifier.uri | https://digitalarchive.library.bogazici.edu.tr/handle/123456789/12279 | |
| dc.publisher | Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2014. | |
| dc.subject.lcsh | Automatic speech recognition. | |
| dc.subject.lcsh | Turkish language -- Morphology. | |
| dc.subject.lcsh | Czech language -- Morphology. | |
| dc.title | Semi-supervised learning based named entity recognition for morphologically rich languages |
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