Abstract
In this demonstration, we present Deep-gAnswer, a knowledge-based question answering system. gAnswer is based on semantic parsing and heuristic rules for entity recognition, relation recognition, and SPARQL generation. By making use of a pre-trained model, we implement new entity and relation recognition networks. Also, it is found that the traditional method works better when information of entity and relation is correctly given. Therefore, we combine entity and relation recognition networks with the previous SPARQL generation process to get Deep-gAnswer. Experimental results show that Deep-gAnswer outperforms the previous one, especially on Chinese dataset.
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