Amharic-English Information Retrieval

Argaw, Atelach Alemu; Asker, Lars

doi:10.1007/978-3-540-74999-8_5

Cited by 6 publications

(6 citation statements)

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“…LM is a popular model for the development of IR systems, but it has not been used in previous Amharic IR ones. Many of them are rather based on vector space model [13,20,32,34]. Here, we investigated the effect of LM model on Amharic IR and compared it with BM25.…”

Section: Resultsmentioning

confidence: 99%

Amharic Semantic Information Retrieval System

Yeshambel

Mothe

Assabie

2022

Communications in Computer and Information Science

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Amharic is the official language of Ethiopia, currently having a population of over 118 million. Developing effective information retrieval (IR) system for Amharic has been a challenging task due to limited resources coupled with complex morphology of the language. This paper presents the development of Amharic semantic IR system using query expansion based on deep neural learning model and WordNet. In order to optimize the retrieval result, we propose Amharic text representation using root forms of words applied for stopword identification, indexing, term matching and query expansion. Comparisons are made with the conventional stem-based text representation for information retrieval, and we show that using the root forms of words is better for both resource construction and system development. The effectiveness of the proposed Amharic semantic IR system is evaluated on Amharic Adhoc Information Retrieval Test Collection (2AIRTC).

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Section: Resultsmentioning

confidence: 99%

Amharic Semantic Information Retrieval System

Yeshambel

Mothe

Assabie

2022

Communications in Computer and Information Science

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“…The average precisions of 0.3615 and 0.4009 were achieved using IDF and stopword list, respectively. Argaw 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].…”

Section: Related Workmentioning

confidence: 99%

“…Few Amharic IR systems were developed in the past. The majority of them are based on stems [26,50,55]; some others are based on citation form [46] and roots [25]. However, due to the complexity of the language, the stem-based, and citation form, and n-gram models do not work well.…”

Section: Comparison With Previous Amharic Ir Studiesmentioning

confidence: 99%

Amharic Adhoc Information Retrieval System Based on Morphological Features

2022

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Information retrieval (IR) is one of the most important research and development areas due to the explosion of digital data and the need of accessing relevant information from huge corpora. Although IR systems function well for technologically advanced languages such as English, this is not the case for morphologically complex, under-resourced and less-studied languages such as Amharic. Amharic is a Semitic language characterized by a complex morphology where thousands of words are generated from a single root form through inflection and derivation. This has made the development of Amharic natural language processing (NLP) tools a challenging task. Amharic adhoc retrieval also faces challenges due to scarcity of linguistic resources, tools and standard evaluation corpora. In this research work, we investigate the impact of morphological features on the representation of Amharic documents and queries for adhoc retrieval. We also analyze the effects of stem-based and root-based text representation, and proposed new Amharic IR system architecture. Moreover, we present the resources and corpora we constructed for evaluation of Amharic IR systems and other NLP tools. We conduct various experiments with a TREC-like approach for Amharic IR test collection using a standard evaluation framework and measures. Our findings show that root-based text representation outperforms the conventional stem-based representation on Amharic IR.

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“…As a result, only few Amharic NLP tools have been developed thus far using rule-based and machine learning approaches. Among the available tools are morphological analyzers [Argaw & Asker, 2006;Gasser, 2011;Abate & Assabie, 2014], stemmer [Alemayehu & Willett, 2003], and parser [Argaw & Asker, 2006;Tachbelie et al, 2011]. However, the development of these tools is at prototype stages.…”

Section: Resourcesmentioning

confidence: 99%

2AIRTC: The Amharic Adhoc Information Retrieval Test Collection

Yeshambel

Mothe

Assabie

2020

Lecture Notes in Computer Science

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Evaluation is highly important for designing, developing, and maintaining information retrieval (IR) systems. The IR community has developed shared tasks where evaluation framework, evaluation measures and test collections have been developed for different languages. Although Amharic is the official language of Ethiopia currently having an estimated population of over 110 million, it is one of the under-resourced languages and there is not yet Amharic adhoc IR test collection. In this paper, we promote the monolingual Amharic IR test collection that we built for the IR community. Following the framework of Cranfield project and TREC, the collection that we named 2AIRTC consists of 12,583 documents, 240 topics and the corresponding relevance judgments.

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Amharic-English Information Retrieval

Cited by 6 publications

References 0 publications

Amharic Semantic Information Retrieval System

Amharic Semantic Information Retrieval System

Amharic Adhoc Information Retrieval System Based on Morphological Features

2AIRTC: The Amharic Adhoc Information Retrieval Test Collection

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