Furrer, Lenz (2014). Unsupervised Text Segmentation for Automated Error Reduction. In: KONVENS 2014, Hildesheim, 8 October 2014 - 10 October 2014. Universität Hildesheim, 178-185.
Challenging the assumption that traditional whitespace/punctuation-based tokenisation is the best solution for any NLP application, I propose an alternative approach to segmenting text into processable units. The proposed approach is nearly knowledge-free, in that it does not rely on language-dependent, man-made resources. The text segmentation approach is applied to the task of automated error reduction in texts with high noise. The results are compared to conventional tokenisation.
Challenging the assumption that traditional whitespace/punctuation-based tokenisation is the best solution for any NLP application, I propose an alternative approach to segmenting text into processable units. The proposed approach is nearly knowledge-free, in that it does not rely on language-dependent, man-made resources. The text segmentation approach is applied to the task of automated error reduction in texts with high noise. The results are compared to conventional tokenisation.
| Item Type: | Conference or Workshop Item (Paper), refereed, original work |
|---|---|
| Communities & Collections: | 06 Faculty of Arts > Institute of Computational Linguistics |
| Dewey Decimal Classification: | 000 Computer science, knowledge & systems
410 Linguistics |
| Uncontrolled Keywords: | Unsupervised Segmentation, , OCR Error Correction |
| Language: | English |
| Event End Date: | 10 October 2014 |
| Deposited On: | 03 Dec 2014 17:18 |
| Last Modified: | 30 Jul 2020 15:19 |
| Publisher: | Universität Hildesheim |
| ISBN: | 978-3-934105-46-1 |
| OA Status: | Green |
| Free access at: | Official URL. An embargo period may apply. |
| Official URL: | http://nbn-resolving.de/urn:nbn:de:gbv:hil2-opus-2893 |
Furrer, Lenz