Log In Sign Up

Building Hierarchies of Concepts via Crowdsourcing

by   Yuyin Sun, et al.

Hierarchies of concepts are useful in many applications from navigation to organization of objects. Usually, a hierarchy is created in a centralized manner by employing a group of domain experts, a time-consuming and expensive process. The experts often design one single hierarchy to best explain the semantic relationships among the concepts, and ignore the natural uncertainty that may exist in the process. In this paper, we propose a crowdsourcing system to build a hierarchy and furthermore capture the underlying uncertainty. Our system maintains a distribution over possible hierarchies and actively selects questions to ask using an information gain criterion. We evaluate our methodology on simulated data and on a set of real world application domains. Experimental results show that our system is robust to noise, efficient in picking questions, cost-effective and builds high quality hierarchies.


page 1

page 2

page 3

page 4


Classifying concepts via visual properties

We assume that substances in the world are represented by two types of c...

Crowdsourcing Relative Rankings of Multi-Word Expressions: Experts versus Non-Experts

In this study we investigate to which degree experts and non-experts agr...

CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos

Crowdsourcing is a valuable approach for tracking objects in videos in a...

Unsupervised Hierarchical Concept Learning

Discovering concepts (or temporal abstractions) in an unsupervised manne...

Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons

We introduce an unsupervised approach to efficiently discover the underl...

DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

Information extraction and user intention identification are central top...