Szamonek Zoltán and Biró István: Similarity based smoothing in language modeling. In: Acta cybernetica, (18) 2. pp. 303-314. (2007)
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Abstract
In this paper, we improve our previously proposed Similarity Based Smoothing (SBS) algorithm. The idea of the SBS is to map words or part of sentences to an Euclidean space, and approximate the language model in that space. The bottleneck of the original algorithm was to train a regularized logistic regression model, which was incapable to deal with real world data. We replace the logistic regression by regularized maximum entropy estimation and a Gaussian mixture approach to model the language in the Euclidean space, showing other possibilities to use the main idea of SBS. We show that the regularized maximum entropy model is flexible enough to handle conditional probability density estimation, thus enable parallel computation tasks with significantly decreased iteration steps. The experimental results demonstrate the success of our method, we achieve 14% improvement on a reail world corpus.
| Item Type: | Article |
|---|---|
| Journal or Publication Title: | Acta cybernetica |
| Date: | 2007 |
| Volume: | 18 |
| Number: | 2 |
| ISSN: | 0324-721X |
| Page Range: | pp. 303-314 |
| Language: | English |
| Place of Publication: | Szeged |
| Event Title: | Conference on Hungarian Computational Linguistics (4.) (2006) (Szeged) |
| Related URLs: | http://acta.bibl.u-szeged.hu/38524/ |
| Uncontrolled Keywords: | Számítástechnika, Nyelvészet - számítógép alkalmazása |
| Additional Information: | Bibliogr.: 314. p. ; összefoglalás angol nyelven |
| Subjects: | 01. Natural sciences 01. Natural sciences > 01.02. Computer and information sciences 06. Humanities 06. Humanities > 06.02. Languages and Literature |
| Date Deposited: | 2016. Oct. 15. 12:25 |
| Last Modified: | 2022. Jun. 16. 14:03 |
| URI: | http://acta.bibl.u-szeged.hu/id/eprint/12818 |
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