Representing phrases – phrase2vec
Encoding words is useful, but usually, we deal with more complex units, such as phrases and sentences. Phrases are important because they specify more detail than just words. For example, the phrase delicious fried rice is very different than just the word rice.
In this recipe, we will train a word2vec model that uses phrases as well as words.
Getting ready
We will be using the Yelp restaurant review dataset in this recipe, which is available here: https://www.yelp.com/dataset (the file is about 4 GB.) Download the file and unzip it in the Chapter03 folder. I downloaded the dataset in September 2020, and the results in the recipe are from that download. Your results might differ, since the dataset is updated by Yelp periodically.
The dataset is multilingual, and we will be working with the English reviews. In order to filter them, we will need the langdetect package. Install it using pip:
pip install langdetect
