###### Abstract  
The main purpose of the joint entity and relation extraction is to extract entities from unstructured texts and extract the relation between labeled entities at the same time. At present, most existing joint entity and relation extraction networks ignore the utilization of explicit semantic information and explore implicit semantic information insufficiently. In this paper, we propose Joint Entity and Relation Extraction **N**etwork with Enhanced Explicit and **I**mplicit Semantic Information (EINET). First, on the premise of using the pre-trained model, we introduce explicit semantics from Semantic Role Labeling (SRL), which contains rich semantic features about the entity types and relation of entities. Then, to enhance the implicit semantic information and extract richer features of the entity and local context, we adopt different Bi-directional Long Short-Term Memory (Bi-LSTM) networks to encode entities and local contexts, respectively. In addition, we propose to integrate global semantic information and local context length representation in relation extraction to further improve the model performance. Our model achieves competitive results on three publicly available datasets. Compared with the baseline model on Conll04, EINET obtains improvements by 2.37% in F1 for named entity recognition and 3.43% in F1 for relation extraction. named entity recognition; relation extraction; semantic role labeling; Bi-directional long short-term memory network; pre-trained model +
Footnote †: journal:  
for text representation. At present, there are a number of approaches that incorporate auxiliary explicit semantic information into some NLP tasks [6; 7], but there is currently a lack of work on using SRL information for joint entity and relation extraction tasks. If a word or phrase is labeled with one semantic role, it is more likely to be labeled as an entity. For example, the semantic role label _ArgM-LOC_ contains location information, which can provide auxiliary information for entity extraction of type _Location_. At the same time, explicit semantic information can cover the semantic relationship between words, which is very useful for relation extraction. Semantic role labeling can provide explicit auxiliary information for NER and RE, and then help the model improve performance. Therefore, we fuse the explicit semantic information obtained by SRL with BERT for joint entity and relation extraction tasks. In addition to ignoring the introduction of explicit semantic information, many existing models also do not adequately explore implicit semantic information. In most of the existing models, the representation vector of text is shared in both entity recognition and relation extraction. However, named entity recognition focuses on the semantic extraction of entities, while relation extraction focuses on the semantic information of the local context which is drawn from the end of the first entity to the beginning of the second entity. Therefore, in order to fully explore the implicit semantic information, we not only adopt Bi-LSTM to further extract implicit semantic information, but also design a novel separately encoded method which adopts two different Bi-LSTMs to enhance the implicit semantic information of the entity and the local context, respectively. It is worth noting that, considering the importance of the context information where the entity pair is located for relation extraction, we introduce the global contextual information based on Bi-LSTM, and enhance semantic information of the local context by adding local context length representation. Enriching the semantic information of the context from global and local perspectives further improves the performance of the model on relation extraction. In general, our contributions can be summarized as follows:  
* We propose a Joint Entity and Relation Extraction **N**etwork with Enhanced **E**xplicit and **I**mplicit **S**emantic Information (EINET).