面向物联网最终用户开发：以烹饪食谱语义解析编程厨房设备为例
Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen Devices
AI总结：
本文构建烹饪食谱到厨房 IoT 命令的标注语料，并采用 CRF 与神经网络序列标注提取四要素语义元组，验证了语义解析的可行性及指令不完整带来的挑战。
摘要：
以支持最终用户对物联网（IoT）进行编程的方式，对用户生成的说明性文本进行语义解析，是一个尚未得到充分探索的领域。在本研究中，我们提供了一个独特的标注语料库，旨在支持将烹饪食谱指令转换为厨房中 IoT 设备可理解的机器命令。每个命令都是一个元组，从“What”（做什么）、“Where”（在哪里）、“Why”（为什么）和“How”（如何做）四个方面捕捉涉及厨房设备的指令语义。基于该语料库，我们开发了基于机器学习的序列标注方法，即条件随机场（CRF）和一种神经网络模型，用于解析食谱指令并从中提取我们关注的元组。结果表明，尽管基于这些标注训练语义解析器是可行的，但大多数自然语言指令并不完整，因此将其转换为形式化意义表示并非易事。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
