Automatic Part of Speech Tagging For, Amharic Language an Experiment Using Stochastic Hidden Markov (Hmm) Approach
| dc.contributor.advisor | Amare, Getahum (Prof.) | |
| dc.contributor.author | Getachew, Mesfin | |
| dc.date.accessioned | 2020-06-12T08:54:14Z | |
| dc.date.accessioned | 2023-11-18T12:45:47Z | |
| dc.date.available | 2020-06-12T08:54:14Z | |
| dc.date.available | 2023-11-18T12:45:47Z | |
| dc.date.issued | 2001-06 | |
| dc.description.abstract | Natural Language processing, as a field of scientific inquiry, plays an important role in increasing computers capability to understand natural languages. Part of speech (POS) tagging is one effort in the task of understanding natural language, the language by which most human knowledge is recorded. The task of POS tagging is to assign unique part of speech tags to sentences that are presented as a linear string of words. POS tagging systems, which annotate corpora written in various languages (e.g. English), are used as components in many applications including phrase recognition, word sense disambiguation, grammatical function assigmnents and many others.Today, taggers of different kinds have been developed for languages, which have relatively wider use nationally and/or internationally. The same story is not true for Amharic, the working language of the Federal Government of Ethiopia, and one of the major languages of Ethiopia (Bender, 1976) for there are no systems (taggers of any sort) that all Notate corpora written in this language. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21543 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Automatic Part of Speech Tagging For, Amharic Language an Experiment Using Stochastic Hidden Markov (Hmm) Approach | en_US |
| dc.type | Thesis | en_US |