Novel Rules for Extracting the Entities of Entity Relationship Models
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June 29, 2021
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Entity Relationship Models , Natural Language Processing, Linguistics Rules, Machine Learning, Logic Based Classifiers, Mapping Nouns into Entities
Abstract
Extracting entities from natural language text to design conceptual models of the entity relationships is not trivial, and novice designers and students can find it especially difficult. Researchers have suggested linguistic rules/guidelines for extracting entities from natural language text. Unfortunately, while these guidelines are often correct, they can also be invalid. There is no rule that is true at all times. This paper suggests novel rules based on the machine learning classifiers, the RIPPER, the PART, and the decision trees. A performance comparison was made between the linguistic and the machine learning rules. The results show that there was a dramatic improvement when machine learning rules were used.
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