Towards End-User Development for IoT: A Case Study on Semantic Parsing
  of Cooking Recipes for Programming Kitchen Devices
Filippos Ventirozos Sarah Clinch Riza Batista-Navarro
Semantic parsing of user-generated instructional text, in the way of enabling
end-users to program the Internet of Things (IoT), is an underexplored area. In
this study, we provide a unique annotated corpus which aims to support the
transformation of cooking recipe instructions to machine-understandable
commands for IoT devices in the kitchen. Each of these commands is a tuple
capturing the semantics of an instruction involving a kitchen device in terms
of "What", "Where", "Why" and "How". Based on this corpus, we developed machine
learning-based sequence labelling methods, namely conditional random fields
(CRF) and a neural network model, in order to parse recipe instructions and
extract our tuples of interest from them. Our results show that while it is
feasible to train semantic parsers based on our annotations, most
natural-language instructions are incomplete, and thus transforming them into
formal meaning representation, is not straightforward.