Paper: Question Answering with Subgraph Embeddings

ACL ID D14-1067
Title Question Answering with Subgraph Embeddings
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few hand-crafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate an- swers. Training our system using pairs of questions and structured representations of their answers, and pairs of question para- phrases, yields competitive results on a re- cent benchmark of the literature.