Information Extraction Using Semantic Relation Learning And Greedy Mapping


Saravadee, Sae Tan (2016) Information Extraction Using Semantic Relation Learning And Greedy Mapping. PhD thesis, Multimedia University.

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In this thesis, the work is motivated to learn the extraction of significant information from natural language text and specify the meanings of the content. The work of information extraction and mapping results in three main challenges. First, to propose a generic and flexible framework that integrates the information extraction and mapping processes into a workflow, such that it can be easily adapted to changes and requirements. Second, to define a representation model that is able to cater various structures with different features and characteristics. Third, to propose a learning algorithm for information extraction and mapping with minimum training effort. In order to address these challenges, a flexible information extraction and mapping framework, SemIE (Semantic-based Information Extraction and Mapping), is proposed. SemIE identifies significant relations from domain-specific text by utilising a semantic structure that describes the domain of discourse.

Item Type: Thesis (PhD)
Additional Information: Call No.: QA76.5913 .S27 2016
Uncontrolled Keywords: Semantic computing
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 25 May 2018 08:17
Last Modified: 25 May 2018 08:17


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