Discovering Implicit Quantity Relations Using Convolutional Network

摘要

This paper presents a novel approach for automatically extracting implicit quantity relations (IQRs) from Chinese algebra word problems (AWPs). Our algorithm first classifies AWPs into categories and analyzes each sentence in the problem to identify the variables and values, then maps them to equations according to the category, and enables its trivial as shown in Figure 1. Our algorithm is able to discover 15 kinds of IQRs in AWPs with an average accuracy of 88% when the training sample accounts for more than 3% of the total. Our data and code are publicly available.¹¹Our data and code is available at: https://pan.baidu.com/s/1g1OI6EZTHCunrvXSzBuTew,Extraction code: zt1y