Generation of representative meal names for food recording data by using web search results
A meal is often called by different names depending on cultures or individuals, and a significantly large number of meal names exist in food recording data from the general public. To analyze the statistics of meals in food recording data, we need to normalize meal names; in other words, make representative names for food recording data. In this paper, we present our approach to obtaining representative names by using both food recording data and web search results. Firstly, we obtained snippets of documents from the web search results for a target meal name. Next, we performed a morphological analysis and recognized recipe-named entities (recipe NEs). Finally, we obtained the most frequent words under the condition that they were included in a candidate list of representative names. We created a dataset and evaluated our proposed method. As a result, 47.96% of Top 1 candidates and 77.32% of Top 5 candidates for generated representative names coincided with those of the ground truth (GT).