Abstract: A computer-implemented system and method and for learning an entity-independent representation are disclosed. The method may include: receiving an input text; identifying named entities in the input text; replacing the named entities in the input text with entity markers; parsing the input text into a plurality of tokens; generating a plurality of token embeddings based on the plurality of tokens; generating a plurality of positional embeddings based on the respective position of each of the plurality of tokens within the input text; generating a plurality of token type embeddings based on the plurality of tokens and the one or more named entities in the input text; and processing the plurality of token embeddings, the plurality of positional embeddings, and the plurality of token type embeddings using a transformer neural network model to generate a hidden state vector for each of the plurality of tokens in the input text.