  Usually, entity relation recognition systems either use a pipe-lined model
that treats the entity tagging and relation identification as separate tasks or
a joint model that simultaneously identifies the relation and entities. This
paper compares these two general approaches for the entity relation
recognition. State-of-the-art entity relation recognition systems are built
using deep recurrent neural networks which often does not capture the symbolic
knowledge or the logical constraints in the problem. The main contribution of
this paper is an end-to-end neural model for joint entity relation extraction
which incorporates a novel loss function. This novel loss function encodes the
constraint information in the problem to guide the model training effectively.
We show that addition of this loss function to the existing typical loss
functions has a positive impact over the performance of the models. This model
is truly end-to-end, requires no feature engineering and easily extensible.
Extensive experimentation has been conducted to evaluate the significance of
capturing symbolic knowledge for natural language understanding. Models using
this loss function are observed to be outperforming their counterparts and
converging faster. Experimental results in this work suggest the use of this
methodology for other language understanding applications.
