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Related Experiment Videos

Morpheme matching based text tokenization for a scarce resourced language.

Zobia Rehman¹, Waqas Anwar, Usama Ijaz Bajwa

¹Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan.

|August 31, 2013

Summary

This study introduces a novel morpheme matching approach for Urdu text tokenization, achieving high precision and recall. The method effectively handles challenges like compound words and affixation in this low-resource language.

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Area of Science:

Natural Language Processing
Computational Linguistics
Information Retrieval

Background:

Text tokenization is crucial for information processing.
Urdu presents unique tokenization challenges due to inconsistent word spacing.
Existing methods struggle with Urdu's linguistic complexities.

Purpose of the Study:

To develop an effective Urdu text tokenization method.
To address issues in boundary detection, compound words, and affixation.
To improve the accuracy of Urdu natural language processing (NLP) tasks.

Main Methods:

A morpheme matching approach was proposed for Urdu tokenization.
Algorithms were developed for compound word boundary detection.
Techniques were implemented to handle affixation, reduplication, names, and abbreviations.

Main Results:

Achieved 97.28% precision.
Achieved 93.71% recall.
Achieved 95.46% F1-measure on a 57,000-word corpus using a 6,400-entry morpheme list.

Conclusions:

The proposed morpheme matching method significantly improves Urdu text tokenization.
The approach is robust in handling complex linguistic features of Urdu.
This advancement facilitates better information processing for Urdu NLP applications.