## 5 Conclusions  
We propose a novel Joint Entity and Relation Extraction **N**etwork with Enhanced Explicit and **I**mplicit Semantic Information (EINET). On the premise of using the pre-trained model, EINET introduces explicit semantic information and fully explores the implicit information for joint entity and relation extraction. Semantic role labelings are vectorized and fused with context representation vectors from BERT. Then, the Bi-LSTM network is used to enhance the implicit semantic information of text. It is worth noting that we adopt different Bi-LSTM networks to capture different features of entities and contexts, respectively. In addition, we introduce the global semantic representation vector based on Bi-LSTM in relation extraction, and add the local context length information to enrich the local semantics. We demonstrate the effectiveness of EINET through comparisons with existing models on three publicly available datasets, and EINET has achieved competitive results. In the next stage, we will further enrich the semantic information of contextual representation based on external knowledge, and explore the common points of joint entity and relation extraction task and other tasks, so as to apply our method to more natural language processing tasks. Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript. This paper was supported by National Key R&D Program of China (2019 YFC1521202). \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{2}{c}{**Error Cases**} & \\ \hline \hline \multirow{2}{*}{Incorrect Spans} & \multirow{2}{*}{Sentences} & \multicolumn{1}{p{113.8 pt}}{Cutaneous necrosis after injection of polyethylene glycol—modified interferon alfa.} \\  & & **Entities**: \{‘type’; _‘Adverse-Effect’_, Cutaneous necrosis} \{‘type’; _‘Drug’_, interferon alfa\} **Relations**: (interferron alfa, _‘Adverse-Effect’_, Cutaneous necrosis) \\  & & **Enties**: \{‘type’; _‘Adverse-Effect’_, Cutaneous necrosis} \{‘type’; _‘Drug’_, interferon} **Relations**: (interferron, _‘Adverse-Effect’_, Cutaneous necrosis) \\ \hline \multirow{2}{*}{Logical} & \multirow{2}{*}{Sentences} & \multicolumn{1}{p{113.8 pt}}{“NRC has a broad programmatic concern that the pressure to meet unrealistic schedule milestones may leave DOE insufficient time to plan and to execute proper technical information-gathering activities.” said [PERSON], chief of waste disposal for the commission.} \\  & & **Enties**: \{‘type’; _‘Org’_, NNC} \{‘type’; _‘Org’_, DOE} \{‘type’; _‘Peop’_, [PERSON]\} **Relations**: ([PERSON], _Work-For_, DOE) \\  & & **Enties**: \{‘type’; _‘Org’_, NNC} \{‘type’; _‘Org’_, DOE} \{‘type’; _‘Peop’_, [PERSON]\} **Relations**: \\ \hline \multirow{2}{*}{Missing Annotation} & \multirow{2}{*}{Sentences} & \multicolumn{1}{p{113.8 pt}}{[PERSON] also fled the Onondaga territory but has since moved to a Shoshone-Bannock reservation in Idaho.}