In various circumstances, the same word can mean differently based on the usage of the word in a particular sentence. The aim of word sense disambiguation (WSD) is to precisely understand the meaning of a word in particular usage. WSD utilized in several applications of natural language to interpret an ambiguous word contextually. This paper enhances a statistical algorithm proposed by Abdo [36] that performs a task of WSD for Afaan Oromoo (one of under-resourced language spoken in East Africa by nearly 50% of Ethiopians). The paper evaluates appropriate methods that used to increase the performance of disambiguation for the language with and without morphology consideration. The algorithm evaluated by 249 sentences with four evaluation metrics: recall, precision, F1 and accuracy. The evaluation result has achieved state of the art for Afaan Oromoo. Finally, future direction is highlighted for further research of the task on the language.

Language is the main means of communication used by human. In various situations, the same word can mean differently based on the usage of the word in a particular sentence which is challenging for a computer to understand as level of human. Word Sense Disambiguation (WSD), which aims to identify correct sense of a given ambiguity word, is a long-standing problem in natural language processing (NLP). As the major aim of WSD is to accurately understand the sense of a word in particular context, can be used for the correct labeling of words in natural language applications. In this paper, I propose a normalized statistical algorithm that performs the task of WSD for Afaan Oromo language despite morphological analysis The propose algorithm has the power to discriminate ambiguous word’s sense without windows size consideration, without predefined rule and without utilize annotated dataset for training which minimize a challenge of under resource languages. The proposed system tested on 249 sentences with precision, recall, and F-measure. The overall effectiveness of the system is 80.76% in F-measure, which implies that the proposed system is promising on Afaan Oromo that is one of under resource languages spoken in East Africa. The algorithm can be extended for semantic text similarity without modification or with a bit modification. Furthermore, the forwarded direction can improve the performance of the proposed algorithm.

Named Entity Recognizer (NER) is a widely used method of Information extraction (IE) in Natural language processing (NLP) and Information Retrieval (IR) aimed at predicting and categorizing words of a given text into predefined classes of Named Entities like a person, date/time, organization, location, etc. This paper adopts boosting NER for Afaan Oromo by using multiple methods. Combinations of approaches such as machine learning, the stored rules, and pattern matching make a system more efficient and accurate to recognize candidates name entities (NEs). It takes the strongest points from each method to boost the system performance by voting a candidate NE which is detected in more than 1 entity category or out of context because of word ambiguity, it penalized by Word senses disambiguation. Subsequent NEs tagged with identical tags merged as a single tag before the final output. The evaluation shows the system is outperformed. Finally, the future direction is forwarded a hybrid approach of rule-based with unsupervised zero-resource cross-lingual to enhance more.