Named entity recognition (NER) and Relation extraction (RE) are two fundamental tasks in natural language processing applications. In practice, these two tasks are often to be solved simultaneously. Traditional multi-task  learning models implicitly capture the correlations between NER and RE. However, there exist intrinsic connections between the output of NER and RE. In this study, we argue that an explicit interaction between the NER model and the RE model will better guide the training of both models. Based on the traditional multi-task learning framework, we design an interactive feature encoding method to capture the intrinsic connections between NER and RE tasks. In addition, we propose a recurrent interaction network to progressively capture the correlation between the two models . Empirical studies on two real-world datasets confirm the superiority of the proposed model.
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification . Empirical studies on two real-world datasets confirm the superiority of the proposed model.
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Named entity recognition (NER) and Relation extraction (RE) are two fundamental tasks in natural language processing applications. In practice, these two tasks are often to be solved simultaneously. Traditional
The idea of using
A
learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task
R
models implicitly capture the correlations between NER and RE. However, there exist intrinsic connections between the output of NER and RE. In this study, we argue that an explicit interaction between the NER model and the RE model will better guide the training of both models. Based on the traditional
techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a
R
learning framework, we design an interactive feature encoding method to capture the intrinsic connections between NER and RE tasks. In addition, we propose a
learning model which we refer to as
R
to progressively capture the correlation between the two models
which allows the learning of interactions dynamically, to effectively model task-specific features for classification