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.

本研究提供了一个独特的注释语料库，旨在支持将烹饪食谱指令转换为厨房物联网设备可理解的机器命令。我们基于该语料库开发了基于机器学习的序列标注方法，如条件随机场和神经网络模型，在从指令中解析并提取我们感兴趣的元组方面取得了可行的结果。然而，我们的结果显示大部分自然语言指令不完整，因此将它们转换为形式化的意义表示并不直接。

面向物联网的最终用户开发: 厨房设备编程的语义解析烹饪配方案例研究