Browsing by Author "Getnet, Berihun"

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    Amharic Information Retrieval Using Semantic Vocabulary
    (Addis Ababa University, 2019-10-02) Getnet, Berihun; Assabie, Yaregal (PhD)
    The increase in large scale data available from different sources and the user’s need access to information retrieval becomes more focusing issue these days. Information retrieval implies seeking relevant documents for the user’s queries. But the way of providing the queries and the system responds relevant results for the user should be improved for better satisfaction. This can be enhanced by expanding the original queries from semantic lexical resources that are constructed either manually or automatically from a text corpus. But, manual construction is tedious and time-consuming when the data set is huge. The way semantic resources are built also affects retrieval performance. Based on formal semantics the meaning is built using symbolic tradition and centered around the inferential properties of languages. It is also possible to automatically construct semantic resources based on the distribution of the word from unstructured data which applies the notion about unsupervised learning that automatically builds semantics from high dimensional vector space. This produces contextual similarity via word’s angular orientation. There have been attempts done to enhance information retrieval by expanding queries from semantic resources for non-Ethiopian languages. In this study, we propose Amharic information retrieval using semantic vocabulary. It isfigured out by considering components including text preprocessing, word-space modeling, semantic word sense clustering, document indexing, and searching. After the Amharic documents are preprocessed the words are vectorized on a multidimensional space using Word2vec based on the notion words surrounding another word can be contextually similar. Based on the word’s angular orientation, the semantic vocabulary is constructed using cosine distance. After Amharic documents are preprocessed it is indexed for later retrieval. Then the user provides the queries and the system expands the original query from the semantic vocabulary. The queries are reformulated and words are searched from indexed data that returns more relevant documents for the user. A prototype of the system is developed and we have tested the performance of the system using Amharic documents collected from Ethiopian public media. The semantic vocabulary based on the word analog prediction using the cosine metric is promising. It is also compared against the semantic thesaurus constructed with the latent semantic analysis and it increases by 17.2% accuracy. Information retrieval using semantic vocabulary based on ranked retrieval increases by 24.3% recall, and using unranked set of retrieval, 10.89% recall improvement was obtained.
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    Amharic Information Retrieval Using Semantic Vocabulary
    (Addis Ababa University, 10/2/2019) Getnet, Berihun; Assabie, Yaregal (PhD)
    The increase in large scale data available from different sources and the user’s need access to information retrieval becomes more focusing issue these days. Information retrieval implies seeking relevant documents for the user’s queries. But the way of providing the queries and the system responds relevant results for the user should be improved for better satisfaction. This can be enhanced by expanding the original queries from semantic lexical resources that are constructed either manually or automatically from a text corpus. But, manual construction is tedious and time-consuming when the data set is huge. The way semantic resources are built also affects retrieval performance. Based on formal semantics the meaning is built using symbolic tradition and centered around the inferential properties of languages. It is also possible to automatically construct semantic resources based on the distribution of the word from unstructured data which applies the notion about unsupervised learning that automatically builds semantics from high dimensional vector space. This produces contextual similarity via word’s angular orientation. There have been attempts done to enhance information retrieval by expanding queries from semantic resources for non-Ethiopian languages. In this study, we propose Amharic information retrieval using semantic vocabulary. It isfigured out by considering components including text preprocessing, word-space modeling, semantic word sense clustering, document indexing, and searching. After the Amharic documents are preprocessed the words are vectorized on a multidimensional space using Word2vec based on the notion words surrounding another word can be contextually similar. Based on the word’s angular orientation, the semantic vocabulary is constructed using cosine distance. After Amharic documents are preprocessed it is indexed for later retrieval. Then the user provides the queries and the system expands the original query from the semantic vocabulary. The queries are reformulated and words are searched from indexed data that returns more relevant documents for the user. A prototype of the system is developed and we have tested the performance of the system using Amharic documents collected from Ethiopian public media. The semantic vocabulary based on the word analog prediction using the cosine metric is promising. It is also compared against the semantic thesaurus constructed with the latent semantic analysis and it increases by 17.2% accuracy. Information retrieval using semantic vocabulary based on ranked retrieval increases by 24.3% recall, and using unranked set of retrieval, 10.89% recall improvement was obtained.