2016/02/12 by Sunil Kumar Jauhar, Sujay Kumar Jauhar, Peter Turney +5 · 13 citations
Computer Science · #Annotation #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Data science #FOS: Computer and information sciences #Identification (biology) #Information extraction #Information retrieval #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Natural language processing #Parsing #Question answering #Range (aeronautics) #Set (abstract data type) #Task (project management) #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1602.03960
published in arXiv (Cornell University) (Cornell University) · Keywords: Data, General Knowledge, Tables, Question Answering, MCQ, Crowd-sourcing, Mechanical Turk
arxiv created 2016/02/12 · openalex publication_date 2016/02/12 · arxiv updated 2016/02/15 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We describe two new related resources that facilitate modelling of general knowledge reasoning in 4th grade science exams. The first is a collection of curated facts in the form of tables, and the second is a large set of crowd-sourced multiple-choice questions covering the facts in the tables. Through the setup of the crowd-sourced annotation task we obtain implicit alignment information between questions and tables. We envisage that the resources will be useful not only to researchers working on question answering, but also to people investigating a diverse range of other applications such as information extraction, question parsing, answer type identification, and lexical semantic modelling.