Firstly PAN employs LSTM to generate representations of sentences s i following<cite> (Shimaoka et al. 2016)</cite> , where s i ∈ R d is the semantic representation of s i , i ∈ {1, 2, ..., n}. Afterwards, we build path-based attention α i,t over sentences s i for each type t ∈ T e , which is expected to focus on relevant sentences to type t. Then, the representation of sentence set S e for type t, denoted by s e,t ∈ R d , is calculated through weighted sum of vectors of sentences.