AIRCC's International Journal of Computer Science and Information Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


The main goal of this work is the implementation of a new tool for the Amazigh part of speech tagging using Markov Models and decision trees.

After studying different approaches and problems of part of speech tagging, we have implemented a tagging system based on TreeTagger - a generic stochastic tagging tool, very popular for its efficiency. We have gathered a working corpus, large enough to ensure a general linguistic coverage. This corpus has been used to run the tokenization process, as well as to train TreeTagger. Then, we performed a straightforward outputs' evaluation on a small test corpus. Though restricted, this evaluation showed really encouraging results.


Keywords

Amazigh, SVM, CRF, HMM, Machine Learning, POS Tagging.
User
Notifications
Font Size