The combination of context information to enhance simple question answering

Abstract

With the rapid development of knowledge base,question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type,outdegree. Experimental results show that context information can improve the result of simple question answering.

References in corpus (12)

• Large-scale Simple Question Answering with Memory Networks
• Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
• What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
• Question Answering with Subgraph Embeddings
• Character-Level Question Answering with Attention
• Open Question Answering with Weakly Supervised Embedding Models
• Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
• Simple Question Answering by Attentive Convolutional Neural Network
• CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
• Freebase-triples: A Methodology for Processing the Freebase Data Dumps
• No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)
• Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification