Normaly Kamal Ismail

A Survey on Context-Aware InformationRetrieval Research

Most of the retrieved documents from the Information Retrieval (IR) System are irrelevant to the user because the IR cannot determine the user’s context. One of the main issues is that the relevancy of the retrieved documents is based on personal assessment that depends on the task to be done and its context. This paper provides the review of prior researches (2003–2016) and concludes the review by providing the summary of the research’s current trends, future direction and opportunity and defining the research gap. First, the findings show that in prior studies, there is no identification of contextual aspect has been done in optimizing the ranking function of the Malay IR. Second, in optimizing the ranking function, the integration process of context representation and document ranking must be done. This approach also has not been done yet in the development of Malay Document Retrieval. If it still stays in the current status, the Malay Document Retrieval system cannot be improved compared to the traditional languages of Context Aware IR System (English).

Enhancing Latent Semantic Analysis by Embedding Tagging Algorithm in Retrieving Malay Text Documents

Latent Semantic Analysis (LSA) is a mathematical approach that uses Singular Value Decomposition to discover the important association of the relationship between terms and terms, terms and documents and also documents and documents. LSA adopted cosine similarity measure to calculate the similarity between the query and terms as well as the documents. This approach seem to be efficient if each of the term only have single meaning and a meaning only represent by single term. Unfortunately, there are terms that have multiple meanings and a single meaning that represent by multiple terms. If these terms are treated as a single word, it will lead the search engine to retrieve the irrelevant documents. The irrelevant documents that been retrieved will affect the effectiveness of the search engine. This paper propose to enhance LSA by embedding tagging algorithm. To investigate the effectiveness of LSA using tagging algorithm in retrieving documents from the Malay corpus, seven experiments are conducted. The first experiment conducted to compare the time taken for extracting the normal term list and the tagged term list, total number of both lists and also the time taken for the creation of term document matrix. All other experiments record all the results of the retrieval system by using different dimension and threshold value. The retrieval result averagely shows F-measure enhancement of approximate to 3.60% by using LSA with tagging algorithm (LSAT) compared to retrieval result of LSA.

Graph-based text representation for Malay translated hadith text

Text representation plays an important role in text classification. Commonly, text representation uses the term frequency technique. Hadith texts consist of two parts; they are the chain of narrators and the content. The term frequency technique is usually used for the content text representation. This research explains the text representation for chain of narrators in hadith texts. The chain of narrators was depicted using graph technique as text representation. A total of 18 hadith texts were used in representing chain of narrators as text representation. The narrators name and relationships between narrators extracted from hadith texts which produced 82 narrator names and 85 connections between narrators. The 82 names were used as nodes, while the 85 connections as the relationships. The text representation graph subsequently used in the hadith text classification research based on chain of narrators.