Figure 4: 
The contextual hypernym prediction model is based on BERT (Devlin et al., 2019). Input sentences ct and cw are tokenized, prepended with a [CLS] token, and separated by a [SEP] token. The target word t in the first sentence, ct, and the related word w in the second sentence, cw, are surrounded by < and > tokens. The class label (hypernym or not) is predicted by feeding the output representation of the [CLS] token through fully-connected and softmax layers.

The contextual hypernym prediction model is based on BERT (Devlin et al., 2019). Input sentences ct and cw are tokenized, prepended with a [CLS] token, and separated by a [SEP] token. The target word t in the first sentence, ct, and the related word w in the second sentence, cw, are surrounded by < and > tokens. The class label (hypernym or not) is predicted by feeding the output representation of the [CLS] token through fully-connected and softmax layers.

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