Surafel, Lemma (PhD)Dagimawi, Demissie2018-10-022023-11-042018-10-022023-11-042017-10http://etd.aau.edu.et/handle/123456789/12386In this paper, Amharic Named Entity Recognition problem is addressed by employing a semi-supervised learning approach based on neural networks. The proposed approach aims at automating manual feature design and avoiding dependency on other natural language processing tasks for classi cation features. In this work potential feature information represented as word vectors are generated using neural network from unlabeled Amharic text les. These generated features are used as features for Amharic Named entity classi cation. SVM, J48, random tree, IBk(Instance based learning with parameter k), attribute selected and OneR(one rule) classi ers are tested with word vector features. Additionally BLSTM(bi-directional long short term memory), LSTM(long short term memory) and MLP(multi layer perceptron) deep neural networks are also tested to investigate the impact of proposed approach. From the experiments the highest F-score achieved was 95.5% using the SVM classi er. Relative to state-of-the-art approaches (SVM and J48) an average F-score improvement of 3.95% was achieved. The results showed that, automatically learned word features can substitute manually designed features for Amharic named entity recognition. Also these features has given better performance while reducing the e ort in manual feature design.en-USAmharic Named Entity RecognitionNeural Word EmbeddingDeep Neural NetworksWord2vecSkip-gram ModelNatural Language ProcessingThesis