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In summary, there are 69 restaurants and 95, reviews in total after the filtering; the number of words per review in average is about and the vocabulary size is 46,{/INSERTKEYS}{/PARAGRAPH} Dataset Our preliminary experiments involve a real-world restaurant review dataset collected from Yelp Data Challenge. In this paper, therefore, we aim to provide a novel framework for automatic dish discovery from restaurant reviews via the embedding techniques. Out of those restaurants, we winnow out the restaurants with a complete menu, setting the reviews of those restaurants and their menus as our dataset. Prelimi- nary experiments are conducted on a real-world restaurant review dataset collected from Yelp Data Challenge. We employ regular expressions to first parse restaurant reviews to extract dish words, and then utilize the processed reviews as training texts to learn embedding vector of each word via the skip-gram model. In the literature, some studies have been conducted with a perspective of finding critical information hidden in the content. We first choose the top restaurants containing the most reviews in the area of Las Vegas and then manually parse the menu of each restaurant from its official website. In the paper, a nearest- neighbor like score function is proposed to rank the dishes based on their learned representations. Then, we utilize the processed reviews as training texts to learn the embedding vectors of words via the skip-gram model. We collect a dataset of user reviews from Yelp and parse the reviews to extract dish words. {PARAGRAPH}{INSERTKEYS}Abstract This paper proposes a novel framework for automatic dish discovery via word embeddings on restaurant reviews. While much has been proposed on accurate sentiment interpretation towards reviews and recommendation, little has focused on dish-level analysis. Background With the growth of social media, corporations, such as Yelp, have accumulated a great number of user generated content UGC. In addition, a nearest-neighbor like score function is proposed to rank the dishes via their learned representations.