Recently, the much celebrated Long-Short Term Memory (LSTM) neural network model has emerged as a successful model to learn representations of sequences, thus providing an ideal solution for many Natural Language Processing tasks whose input is sequence-based, e.g., sentences and phrases (Hill et al., 2016;Melamud et al., 2016;Peters et al., 2018).
in marked contrast to the other LSTM-based approaches in the literature, we use sensetagged text to provide input contexts of the kind s i−W , .