Ma et al. (2016) 49.30 68.23 61.27 AFET (Ren et al., 2016a) 55.10 71.10 64.70 FNET (Abhishek et al., 2017) 52.20 68.50 63.30 NEURAL<cite> (Shimaoka et al., 2017)</cite> This indicates the benefits of our proposed model architecture for learning fine-grained entity typing, which is discussed in detail in Section 3.4; and (3) BINARY and KWASIBIE were trained on a different dataset, so their results are not directly comparable.