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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Faili, H. Ravanbakhsh, H. |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept. ECE, University of Tehran, Iran (Faili, H.; Ravanbakhsh, H.) |
| Abstract | Language modeling is used in many NLP applications like machine translation, POS tagging, speech recognition and information retrieval. It assigns a probability to a sequence of words. This task becomes a challenging problem for high inflectional languages. In this paper we investigate standard statistical language models on the Persian as an inflectional language. We propose two variations of morphological language models that rely on a morphological analyzer to manipulate the dataset before modeling. Then we discuss shortcoming of these models, and introduce a novel approach that exploits the structure of the language and produces more accurate. Experimental results are encouraging especially when we use n-gram models with small training dataset. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 215856 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424468966 |
| e-ISBN | 9781424468997 |
| DOI | 10.1109/NLPKE.2010.5587823 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Vocabulary morphological Tracking Computational modeling Speech recognition Probability Persian n-gram Data models language model Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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