Publication: Using Probability Theory to Identify the Unsure Value of an Incomplete Sentence
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Most of the existing techniques on relation extraction focus on extracting relation between subject, predicate and object in a single sentence. However, these techniques unable to handle the situation when the text has sentences that are incomplete: either does not have or unclear subject or object in sentence (i.e. 'unsure' value). Thus this does not properly represent the domain text. This paper proposes an approach to predict and identify the unsure value to complete the sentences in the domain text. The proposed approach is based on the probability theory to identify terms (i.e., subject or object) that are more likely to replace the 'unsure' value. We use voting machine domain text as a case study. � 2015 IEEE.
Description
Keywords
Incomplete information system, most dominant, non-taxanomic, predicate, probability theory, Circuit simulation, Computer science, Computers, Software engineering, Incomplete information systems, most dominant, non-taxanomic, predicate, Probability theory, Probability