Comparison of a rule-based approach with a machine-learning-based approach for semantic parsing
Semantic analysis is a very important part of natural language processing that often relies on statistical models and supervised machine learning approaches. However, these approaches require resources that are costly to acquire. This paper describes our experiments to compare Anasem, a Prolog rule-based semantic analyzer, with the best system of the Conference on Natural Language Learning (CoNLL) shared task on semantic analysis. Both CoNLL best system and Anasem are based on a dependency representation, but the major difference is how the two systems extract their semantic structures (rules versus machine learning). Our results show that a rule-based approach might still be a promising solution able to compete with a machine learning system under certain conditions.
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Comparing a Rule-Based and a Machine Learning Approach for Semantic Analysis
The Sixth International Conference on Advances in Semantic Processing (SEMAPRO 2012), Barcelone