Browsing by Author "Moges, Girma"

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Security Exception under Ethiopian Bilateral Investment Treaty
    (Addis Ababa, Ethiopia, 2021) Moges, Girma
    Bilateral investment treaties are legal tools under international law between two contracting countries, the aim of which is to set up understandable, easy, and enforceable rules for the reciprocal protection of foreign investment. Contracting parties have an obligation to enforce BITs like other treaties. However, balancing protections for investment and investor on one hand and national security on the other hand is the area which needs attention for Ethiopia. Thus, this study would deal with how Ethiopian BITs give protection for security of the nation while signing and executing BITs by reviewing investment law, BITs signed by Ethiopia with other sovereign states and other relevant literatures. Ethiopia needs to have a specific and specified national security exception in both its domestic investment laws and BITs, according to the researcher.
  • No Thumbnail Available
    Item
    Semantic-Aware Amharic Text Classification Using Deep Learning Approach
    (Addis Ababa University, 10/10/2020) Moges, Girma; Assabie, Yaregal (PhD)
    Now we are at the age of information era, information is stored, extracted, and used in different formats. Text can be an extremely rich source of information, but extracting insights from it can be hard and time-consuming due to its unstructured nature. Text classification is one of the methods used to organize massively available textual information in a meaningful context to maximize the utilization of information. Amharic text classification is done using the classical and traditional machine learning approaches with a limitation of semantic representation and use of high engineered feature extraction. However, the newly emerged deep learning approach and the use of word embedding improves the performance of text classification through extracting features automatically and represent words semantically in sparse vector. Thus, we develop the LSTM model to train our data and to make the classification process. The classification of the Amharic text documents using the LSTM pass through the process of; preprocessing, word-embedding, deep network building, output determination, training the model, and classification. The semantics of document is done using word2vec, to map similar words in to a single vector using neural network architecture. Thus, the vector representations of words are used as the input for the dep network building component. The model is evaluated using accuracy and loss by training, testing, and validation dataset and resulted 92.13 testing accuracy, and 86.71 validation accuracy.
  • No Thumbnail Available
    Item
    Semantic-Aware Amharic Text Classification Using Deep Learning Approach
    (Addis Ababa University, 2020-10-10) Moges, Girma; Assabie, Yaregal (PhD)
    Now we are at the age of information era, information is stored, extracted, and used in different formats. Text can be an extremely rich source of information, but extracting insights from it can be hard and time-consuming due to its unstructured nature. Text classification is one of the methods used to organize massively available textual information in a meaningful context to maximize the utilization of information. Amharic text classification is done using the classical and traditional machine learning approaches with a limitation of semantic representation and use of high engineered feature extraction. However, the newly emerged deep learning approach and the use of word embedding improves the performance of text classification through extracting features automatically and represent words semantically in sparse vector. Thus, we develop the LSTM model to train our data and to make the classification process. The classification of the Amharic text documents using the LSTM pass through the process of; preprocessing, word-embedding, deep network building, output determination, training the model, and classification. The semantics of document is done using word2vec, to map similar words in to a single vector using neural network architecture. Thus, the vector representations of words are used as the input for the dep network building component. The model is evaluated using accuracy and loss by training, testing, and validation dataset and resulted 92.13 testing accuracy, and 86.71 validation accuracy.