# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 1 Introduction  
Searching digital information on the Web or a large corpus has become part of the human daily life. Information Retrieval (IR) is concerned with searching relevant documents to a user's query from a document collection. Both the research community and the industry have been very active in this field for more than 60 years [1]. Nowadays, IR has gained much attention due to the explosion of digital data and the need of accessing relevant information from huge corpus quickly and accurately. The ability of an IR system to retrieve relevant documents effectively should be tested and its performance should be evaluated systematically. Performance evaluation of an IR system is indeed very crucial for scientific progress [2, 3]. IR has an old history of evaluation. In _adhoc_ retrieval, where the task for the system is to retrieve relevant documents for a given query within a fixed size corpus, both the evaluation framework and effectiveness measures are well established. Test collections are the most widely used resources for performance evaluation [4]. A standard _adhoc_ retrieval test collection consists of three components: a corpus of documents to be searched in, a set of users' information needs or topics, and the associated relevance judgments indicating which documents are relevant for which topics. A large number of shared tasks rely on such collections. Some of the well-known text collections and evaluation programs are Cranfield project [5], Text REtrieval Conference (TREC) and more specifically TREC _adhoc_[6], Cross-Language Evaluation Forum (CLEF) [3],and NACSIS Test Collection for Information Retrieval (NTCIR) [7]. The IR international conferences such as TREC, CLEF, NTCIR, INEX, and FIRE are held based on their own test collections. IR systems work based on documents produced in natural language and consequently, the performance of IR is affected by the characteristics of the language [8]. As a result, NLP has been employed to improve the effectiveness of IR systems. For example, NLP tools and resources provide a means to find index terms and query terms that improve search results. Linguistic variation has significant impact on IR effectiveness as it leads to the omission of relevant documents to users' queries [8]. This calls for the need of dealing with language specific issues to improve the performance of IR systems. Thus, NLP has long attracted the attention of the IR community [9; 10; 11]. The morphology, orthography, tokenization, syntax, semantics, and compound splitting of a language are some of the issues to be considered while developing an IR system. Many languages have several word forms generated from a single word due to morphology and orthography. Identifying the basic units of words is more difficult for morphologically complex languages than for simple languages [12]. Performing simple matching between words generated from the same root is not sufficient while retrieving relevant documents to a user query [13]. Numerous researches have been conducted to develop IR system, to construct resources and corpora for resourceful languages such as English, French, and German. This is not, however, the case for under-resourced languages such as Amharic [14]. Amharic is one of the under-resourced languages where, in comparison with technologically advanced languages, few attempts have been made to develop Amharic NLP tools, applications, resources and corpora [15; 16; 17]. Among the few efforts are the development of stemmer [18; 19], morphological analyzer [16; 20; 21; 22], part-of-speech tagger [15; 23; 24], resources [25; 26; 27], and corpus [28; 29; 30]. However, the existing tools, resources and corpora are not fully functional, limiting their usability for IR [31]. Furthermore, the number of studies reported on Amharic IR and related applications are considerably limited compared to what is carried out for other languages such as English. The morphological complexity of Amharic creates a challenge on the development and retrieval effectiveness of Amharic IR.