Lean Question Answering over Freebase from Scratch
Xuchen Yao · 2015
For the task of question answering (QA) over Freebase on the WEBQUESTIONS dataset (Berant et al., 2013), we found that 85% of all questions (in the training set) can be directly answered via a single binary relation.Thus we turned this task into slot-filling for tuples: predicting relations to get answers given a question's topic.We design efficient data structures to identify question topics organically from 46 million Freebase topic names, without employing any NLP processing tools.Then we present a lean QA system that runs in real time (in offline batch testing it answered two thousand questions in 51 seconds on a laptop).The system also achieved 7.8% better F 1 score (harmonic mean of average precision and recall) than the previous state of the art.