# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 3 Related Work
On this small collection, the average precision and recall values they report are 0.65 and 0.95, respectively, using OR operator in between query terms, and 0.99 and 0.52, respectively, for AND operator. [PERSON] et al. [45] developed dictionary-based Amharic-English IR system. Documents and queries are represented using bag-of-words. In their work, stopwords are removed using Inverse Document Frequency (IDF) and stopwords list. The average precisions of 0.3615 and 0.4009 were achieved using IDF and stopword list, respectively. [PERSON] et al. [46] also have developed dictionary-based Amharic-French IR system with and without word sense discrimination using bag-of-words approach. Word sense discrimination refers to the distinction between different word meanings without sense assignment [47]. Stemming was applied to remove prefix and suffix. The experiments were conducted on the Swedish Institute of Computer Science (SICS) and Lucene search engines. Stopwords were removed by using IDF. The authors presented that the results are better when using SICS than Lucene. The word sense discrimination performs slightly better than non-discrimination. The effect of query expansion using word embedding was evaluated in some researches. The impact of query expansion on IR based on NLP tools and Word2 Vec embedding algorithms was verified in [48]. Continuous Bag of Word (CBOW) and Skip-gram models were trained on TREC corpus and used to select semantically related terms to the initial query terms. In order to obtain better retrieval result, the two word embedding models were combined with stemming. Okapi BM25 probabilistic weighting scheme with default parameters was used for ranking retrieved documents. The experiment was conducted by running 72 queries. The mean average precision (MAP) 0.1769 and 0.128 are obtained for Skip-gram and CBOW models, respectively. Whereas the Normalized Discount cumulative Gain (NDCG) values 0.4576 and 0.3841 are obtained for skip-gram and CBOW models, respectively. The use of local and global word embedding in query expansion for _adhoc_ IR was studied in [49]. The idea was to evaluate the effect of the local word embedding CBOW and the global word embedding GloVe on query expansion. Various experiments were conducted on TREC12, Wiki, news, web, and robust corpora after stemming using Language Modeling (LM) approach. The result indicates that query expansion using local embedding is better than global embedding for IR. However, the retrieval effectiveness of query expansion using local embedding is poor. Amharic semantic-based IR system was developed using BM25 model [50]. Documents and queries were processed by a stemmer. The retrieval effectiveness of the system was evaluated by running 10 queries on 8759 documents and performed an average recall and precision of 0.84 and 0.23, respectively. [PERSON] et al. [51] developed Arabic document retrieval system based on word-embedding and prospect-guided as query expansion techniques. The deep averaging network in vector space model, the probabilistic Okapi BM25 model and the two representative word embedding methods for automatic query expansion was used. Various experiments were carried on TREC test collections. Stemming was performed and has shown a significant impact on the performance of Arabic text retrieval. Their finding indicates that deep average network could not improve performance using only BM25. However, the retrieval performance outperformed all baselines after integrating the two query expansion techniques. While there are several studies that focused on the development of IR systems for Semitic languages, most of them have followed the techniques employed for morphologically simple languages such as English. This has not produced the desired retrieval result as documents could not be represented appropriately. Only few studies have tried to consider the issue of document representation in a systematic way [13]. In our approach, we address this crucial issue of document representation in the development of Amharic IR.