We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F 1 by 7 points.
For open domain question answering, REF proposed the method to map an interrogative sentence on an entity and a relation type contained in a knowledge base by using CNN.
Semantic Parsing for Single-Relation Question Answering
ACL
Computer Science