# A Text-Generated Method to Joint Extraction of Entities and Relations
## 1 Introduction  
Entity-relation extraction is the core task and important segment in the fields of information extraction, knowledge graph, natural language understanding, etc. In recent years, knowledge graph [1] has been widely applied. Many achievements have also been made in the downstream tasks such as question answering and retrieval based on knowledge graph. The basis for constructing the knowledge graph is to build a knowledge base. In the knowledge base, the structured relational triples are preserved in formats such as _<entity 1_, _rel_, _entity 2_>, which means that there is a relation _rel_ between _entity 1_ and _entity 2_. The goal of entity-relation extraction task is to extract the semantic relations between entity pairs from unstructured text. With the application of deep learning in joint learning and distant supervision, the relation extraction task has obtained rich research results. The supervised entity-relation extraction methods can be divided into pipeline and joint learning. The pipeline methods take the named entity recognition (NER) and relation classification (RC) as two separate subtasks, and extract the relations between entities based on the completion of entity recognition [2; 3; 4]. However, this kind of methods ignores the relevance between these two subtasks. Recent work using joint learning [5; 6; 7; 8] can make use of the tight interaction information between entities and relations and use a single model to extract entities and classify the relations between entities simultaneously, which solves the problems of the pipeline method well. However, most of the existing work often requires complex feature engineering or relies heavily on the NLP tools to extract features. Moreover, most existing relation extraction models focus on scenarios dealing with a single relation within one sentence, but there are usually multiple relations between entity mentions in onesentence. Sentences can be divided into three classes based on the degree of entity overlap [9], as shown in Table 1: (1) Normal: a sentence belongs to Normal class if none of its triplets have overlapped entities; (2) Entity Pair Overlap: some of its triplets have overlapped entity pair; (3) Single Entity Overlap: some of its triplets have an overlapped entity and these triplets do not have overlapped entity pair. Even though there are already several works to address the triplet overlap issue [9; 10; 11], their effects are far from good enough, cannot solve the problems of relation extraction in complex situations very well. As a result, the triplet overlap issue is not actually addressed. In reality, natural language texts, such as news and blogs, usually express multiple relations and it is also common that one or more entity mentions appear among multiple relations. Therefore, it is necessary to extract overlapping relations from the perspective of practical application scenarios. The overlapping multi-relation extraction problem is more complex than single-relation extraction because the single-relation extraction scenario can be basically divided into the following two types: (1) for sentences with the given entity pairs, relation classification can be modeled as a text classification problem; (2) for sentences with non-annotated target entity pairs, the model assumes that sentence contains only one pair of entities and relation classification is performed after entity recognition; these two cases usually use the _softmax_ function in the relation classification phrase, so only one relation can be extracted. In the multi-relation extraction situation, we need to find every complete relational triple. The model needs to simultaneously extract the relation and the corresponding entity pairs. Reference [8] provided an idea to integrate entity mention and relation type information into each label, this two information can be obtained simultaneously when tagging each word.