## 1 Introduction  
For the development of applications with semantic capabilities, models such as word embeddings and distributional semantic representations play an important role. These models are the building blocks for a number of natural language processing (NLP) applications. Recently, with the advent of more computing power and the widespread availability of a large number of texts, pre-trained models are becoming commonplace. The availability of pre-trained semantic models allows researchers to focus on the actual NLP task rather than investing time in computing such models. In this work, we consider semantic models as the techniques and approaches used to build word representations or embeddings that can be used in different downstream NLP applications.  
The work by [1] indicates that word-level representations or word embeddings have played a central role in the development of many NLP tasks. For a named entity recognition task, there are many works that indicate word2 Vec lead to a performance boost [2; 3; 4; 5]. Static word-embedding models have been also integrated for several NLP tasks such as sentiment analysis [6; 7], part-of-speech (POS) tagging [8; 9], semantic composi-tionality [10; 11], and many more. While static word-embedding models fail to capture contextual information regarding ambiguous words, the introduction of BERT [12] and similar models have addressed this limitation. The work by [13] indicates that BERT was able to represent the traditional NLP pipeline in an interpretable way, covering some of the basic NLP tasks such as POS tagging, NER, semantic roles, and co-reference resolution.  
Even though getting text data is not a problem for low-resource languages, there are only limited efforts in releasing pre-trained semantic models [14; 15]. In the case of Amharic, there are very few pre-trained models, for example fastText [16], XLMR [17], and Multi-Flair [18]. Also, these models are produced as part of multilingual and cross-lingual experimental setups, which will not fit the needs of most NLP tasks [14].  
In this paper, we have surveyed the existing NLP tasks for Amharic, including available datasets and trained models. Based on the insights on the current state-of-the-art progress on Amharic NLP, we performed different experiments specifically on the integration of semantic models for various tasks, particularly parts-of-speech (POS) tagging, named entity recognition (NER), sentiment analysis, word relatedness, and similarity computation.  
The main contributions of this work are many folds: (1) Surveying the existing NLP tasks and semantic models. (2) Computing and fine-tuning nine semantic models and the release of the models publicly along with benchmark datasets for future research. (3) Investigating the main challenges in the computation and integration of semantic models for the Amharic text. (4) Implementing the first Amharic text segmenter and normalizer component and releasing it along with the models and datasets. (5) Release of the word similarity and relatedness datasets (WordSim353 and SimLex99) that have initially been translated using Google Translate API and subsequently have been validated by native speakers. Table 1 shows the different resources (models, tools, datasets) we have contributed. The different strategies and methods used to collect the different dataset and corpus are presented in Section 1.1.2.