Machine Learning Approach for Morphological Analysis of Tigrigna Verbs
| dc.contributor.advisor | Getachew Alemu (PhD) | |
| dc.contributor.author | Gebrearegay Kalayu | |
| dc.date.accessioned | 2024-04-25T08:41:31Z | |
| dc.date.available | 2024-04-25T08:41:31Z | |
| dc.date.issued | 2018-10 | |
| dc.description.abstract | Morphology, in linguistics, is the study of the forms of words that deals with the internal structure of words and word formation. Morphological analysis is the basic task of natural language processing that is defined as the process of segmenting words into morphemes and analyzing the word formation. It is often an initial step for various types of text analysis of any languages. Rule-based approach and machine learning approach are basic mechanisms for morphological analysis. The rule-based method is popular for the analysis but has limitations in terms of the efforts needed and the time. This is because the languages have many rules for a single word especially in the case of verbs. It is also difficult to include all words that need independent rules which limits the rule-based approach to accommodate words that are not in the database of the systems which can also affect the efficiency of the systems. In this work, a system for morphological analysis of Tigrigna language verbs is designed and implemented using machine learning approach. It is intended to automatically segment a given input verb into morphemes and give their categories based on prefix-stem-suffix segmentation. It gives the inflectional categories based on the subject and object markers of verbs that includes the gender, number and person by detecting the correct boundary of the morphemes. The negative, causative and passive prefixes are also considered. The data needed for training and testing was collected from scratch and annotated manually as the language is under-resourced. After the annotation process, an automatic method was implemented using java to preprocess the annotated verbs to produce list of instances for training and testing. The instance- based algorithm was used with the overlap metric with information gain weighting (IB1-IG) and without weighting (IB1) the features. Experiments were performed by varying the number of nearest neighbors starting from one up to seventeen where the accuracies were almost saturated for both the IB1 and IB1-IG. The majority class voting and the inverse distance weighted decision methods were also compared in the experiment. The best performance were obtained with IB1 using both decision methods when the number of nearest neighbors parameter was smaller. The performance decreased as the number of nearest neighbor increased for both decision methods but showed higher variation in the case of majority class voting. Similarly, the performance with IB1-IG was also better for the smaller number of nearest neighbor for both decision methods and decreased when the number of nearest neighbor increased where it showed higher decrement in the case of majority voting. The IB1 achieved better performance compared to the IB1-IG. A highest accuracy of 91.56% and 89.15% was achieved using IB1 and IB1-IG, respectively with the number of nearest neighbor parameter of 1 for IB1 and 2 for IB1-IG. This encouraging result revealed that the instance-based algorithm is able to automate the morphological analysis of Tigrigna verbs. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/2865 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Morphological analysis | |
| dc.subject | Tigrigna verbs | |
| dc.subject | data annotation | |
| dc.subject | Instance-based | |
| dc.subject | Accuracy | |
| dc.title | Machine Learning Approach for Morphological Analysis of Tigrigna Verbs | |
| dc.type | Thesis |