# An Easy Partition Approach for Joint Entity and Relation Extraction
## 1 Introduction  
Triplet extraction (TE) aims at extracting the entities and relations between entity pairs to form triples in the given raw text. It is useful for many applications, especially those that require extracting the critical information from a given text. It is related to two other tasks: named entity recognition (NER) [1] and relation classification (RC) [2].  
Triplet extraction has been widely studied in the existing work from two perspectives. One is multitask learning perspective: entity recognition and relation classification are regarded as two separate tasks [3], and two independent encoders are used to learn the features of NER [4; 5; 6] and RC tasks [7; 8; 9]. Despite the effectiveness of these approaches, the correlation between the features of the two tasks is absent, and thus may lead to error propagation between tasks. In order to mitigate these issues, a single-task perspective is proposed, which typically designs a unique structure to represent the two tasks [10; 11; 12], such as TPLinker [13] and UniRE [14]. These approaches integrate NER and RC as one task, and generally use a unified encoder to learn the unified feature representation. These approaches avoid error propagation, but they may not capture the specific information needed for each task. In addition, a uniform feature was empirically shown to cause the interference issue [3].  
To obtain improved task-specific features, the authors of [15] proposed a neuron-level partition filter network that learns three feature representations at the neuron level, and the structure has been proven effective in multiple tasks, but the method is a bit complex. The pretraining language model (PLM) has achieved state-of-the-art performance in various NLP tasks, including machine translation [16; 17] and sentiment analysis [18]. To leverage the latent contextual space of PLM, we propose a simple but effective approach, called easy partition approach for relation extraction (EPRE). Unlike state-of-the-art alternativePFN [15], our proposed EPRE is different in the following aspects: (I) We do not construct a unique partition network based on neurons, but directly and simply partitions the results of PLM based on tokens. Specifically, our feature partition leverages the contextual representations of PLMs and performs feature partitioning on the token level to capture task-specific features. (II) We add a gate module to control the information flow between the NER and RC tasks and enhance the model flexibility. We discard the recursive structure and speed up the training process. As a result, our EPRE is simpler and more time-saving. To validate the effectiveness of EPRE, we empirically conduct extensive experiments on various benchmark datasets. Our experimental results show that EPRE is promising. In brief, we summarize our contributions, as follows:  
* We propose a simple but effective joint coding relation extraction approach, namely, easy partition approach for relation extraction (EPRE). Particularly, EPRE models the features between NER and RC tasks to ensure the independence and interaction of specific tasks.
* We consider different gate modules to fuse task features to increase the elasticity of the model.
* Our method is superior in speed, and yields the state-of-the-art results of the NER task and RC task on some benchmark datasets.