Resource Lenient Approaches to Cross Language Information Retrieval : Using Amharic
Abstract: Information Retrieval (IR) deals with finding and presenting information from a collection of documents/data that are relevant to an information need (a query) expressed by a user. Cross Language Information Retrieval (CLIR) is a subfield of IR where queries are posed in a different language than that of the document collection. Computational linguistic tools and resources are essential to accomplish the tasks in CLIR and to date, CLIR research is dominated by a very limited number of languages for which such tools and resources are available. In order to facilitate global information sharing, it is important to enable access to information using as many languages as possible. This requires an investigation into the feasibility of CLIR for languages with a limited set of computational linguistic resources.Amharic is a well-studied language with a rich history and culture, but has very limited computational linguistic tools and resources. This dissertation provides an in depth investigation into a CLIR system for Amharic (against English and French document collections). Scalable techniques were developed to accomplish Amharic CLIR tasks and each task was evaluated individually as a stand alone experiment. Large scale IR experiments were then conducted in order to evaluate the effect of three parameters, namely, transliteration, word sense discrimination, and term selection based on part of speech tags, on the overall IR performance. The effects were measured by individually tuning each of these parameters through a series of benchmarking experiments, geared towards optimizing retrieval precision as well as recall. The results give an insight into the performance of the chosen approaches, the challenges, and their impact on the overall IR performance.
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