# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 3 Related Work  
Semitic languages are known to pose unique challenges in the development of NLP applications due to their complex morphologies. These challenges are propagated to the development of IR systems since the effectiveness of IR systems depends on the availability of various NLP tools and resources. In this section, we discuss the techniques and NLP resources used to develop IR systems for Semitic languages in general. Arabic is the largest of the Semitic language family. Arabic IR systems have relatively a long history [37; 38; 39]. [PERSON] [13] investigated the effectiveness of word-based, stem-based, and root-based representation of documents and queries. The word-based and stem-based text representations miss relevant documents while root-based representation retrieves non-relevant documents. [PERSON] [8] compared the effects of stem and root on Arabic IR. The results of their experiments indicate that the use of stem is more effective than root. [PERSON] et al. [37] investigated the effects of light stemming (removal of prefix and suffix) on Arabic IR. A comparison was made between stem-based and root-based retrieval. The finding indicates that light stemmer outperforms root analyzer and other stemmers which are based on detailed morphological analysis. [PERSON] [40] presented an Arabic text retrieval technique using lexicon-based light stemming. The study evaluated the effectiveness of lexicon-based light stemming, Arabic patterns, root, expanding query and filtering foreign words using n-grams on TREC 2001 test collection. According to the results, the preprocessing techniques such as normalization, stopword removal and light-stemming improve retrieval results whereas n-grams and roots decrease the performance. [PERSON] et al. [41] developed a neural network-based model where documents and queries are represented using stems and their similarity is computed using cosine similarity. The lexicon-based stemming and the relevance feedback approaches perform better than light-stemming approach alone. [PERSON] et al. [42] investigated the effect of morphological analysis on Arabic IR. A rule-based stemmer was used to extract the root/stem of words to be used as indexing and searching terms. The results showed slight improvement on IR effectiveness due to the stemmer. Hebrew is another Semitic language, spoken mainly in Israel. [PERSON] [43] presented a morphological disambiguation tool based on a statistical approach that takes advantage of an existing morphological analyzer. The approach is context-free and was used for query analysis and linguistic indexing of text documents. Instead of words, the morphological patterns were used for disambiguation. The statistical morphological disambiguator returns only the best base form(s), or lemma(s). It makes the decisions of the most likely set of analyses based on the frequency of the morphological patterns associated with the analyses of the input word. The disambiguator was tested by integrating with the Hebrew search engine. It confates all inflectional forms and the performance of the search engine increased. [PERSON] [44] designed Hebrew search engine by applying a rule-based morphological analysis. The design of the search engine considers the construction of morphological, syntactic and semantic analyzers. The search engine eliminates words unsuited both to the syntax and the semantics of a sentence. Although Amharic is significantly used in Ethiopia, the status of IR system development for the language is relatively at rudimentary level. [PERSON] and [PERSON] [25] studied the retrieval effectiveness of word-based, stem-based, and root-based text representations on Amharic language. The experiments were carried out by running 40 queries on 548 documents using OKAPI system and the study concludes that stem-based retrieval is slightly better than root-based. [PERSON] et al. [26] developed an Amharic search engine using stems. The system was tested with 11 queries on 75 news documents.