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首页> 外文会议>IEEE International Conference on Semantic Computing >Interactive Clustering of Cooking Recipe Instructions: Towards the Automatic Detection of Events Involving Kitchen Devices
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Interactive Clustering of Cooking Recipe Instructions: Towards the Automatic Detection of Events Involving Kitchen Devices

机译：烹饪配方的交互式聚类说明：朝向涉及厨房设备的事件的自动检测

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摘要

Cooking recipes are a rich source of semantic information. They contain instructions for food preparation tasks, specifying the actions that should be carried out which typically involve various ingredients and kitchen devices. In an IoT scenario, instructions in cooking recipes can form the basis for automatically controlling kitchen devices without any programming. However, as these instructions are written in natural language, they first need to be transformed or parsed into machine-interpretable commands. As a step towards this, we investigate methods for identifying the various types of actions (events) that kitchen devices are involved in. We cast this problem as a clustering task, whereby recipe instructions involving a given device of interest, are automatically grouped according to the type of event described. Each sentence in every instruction is represented by its embedding vector which is computed using a BERT-based model, specifically one pre-trained using a Roberta architecture. We cluster these sentence embeddings using our newly proposed interactive machine learning (IML)-based framework underpinned by the HDBScan clustering technique. We demonstrate that our IML framework can detect events in sentences with satisfactory accuracy, reaching almost the same level as human performance.
机译：烹饪食谱是一个丰富的语义信息来源。它们包含用于食品准备任务的说明，指定应进行的操作，通常涉及各种成分和厨房设备。在IOT方案中，烹饪配方中的说明可以在没有任何编程的情况下自动控制厨房设备的基础。但是，由于这些指令以自然语言编写，因此他们首先需要转换或解析为机器可解释的命令。作为对此的一步，我们调查识别厨房设备涉及的各种类型的方法（事件）。我们将此问题作为群集任务，由此涉及给定的感兴趣设备的食谱指令，自动根据所描述的事件类型。每个指令中的每个句子由其嵌入式向量表示，其使用基于BERT的模型来计算，特别是使用Roberta架构预先训练的媒体。使用我们的新提出的交互式机器学习（IML）基于HDBSCAN聚类技术基本的框架群集这些句子嵌入。我们展示了我们的IML框架可以以满意的精度检测句子中的事件，达到与人类性能几乎相同。

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