Joint Entity and Relation Extraction Based on Bidirectional Update and Long-Term Memory Gate Mechanism (2024)

Abstract: “Joint entity recognition and relation extraction are important tasks in natural language process-ing. While some previous work has recognized the importance of relation information in jointextraction, excessively focusing on relation information without utilizing entity information maylead to information loss and affect the identification of relation tuples. Additionally, ignoring theutilization of original information may result in the loss of hierarchical and semantic information,further reducing the richness of information.To address these issues, we propose a bidirectionalinformation updating mechanism that integrates entity and relation information, iteratively fus-ing fine-grained information about entities and relations. We introduce a long-term memory gatemechanism to update and utilize original information using feature information, thereby enhanc-ing the model’s ability for entity recognition and relation extraction. We evaluated our approachon two Chinese datasets and achieved state-of-the-art results.”

## related work the traditional pipeline extraction approach @xcite sequentially extracts entities and relationships. although flexible and simple, it faces two main issues. first, there is error propagation due to the independent nature of entity recognition and relationship extraction stages. errors in entity recognition cannot be corrected during the relationship extraction stage, leading to irreversible impacts on the model's ability to accurately extract relationships. second, there is a lack of information interaction between entity recognition and relationship extraction tasks, overlooking the inherent connection between entities and relationships. to address these issues, some scholars have proposed the concept of joint extraction to enhance the accuracy of extraction by strengthening the correlation between entity recognition and relationship extraction. @xcite proposed an end-to-end joint extraction model where the authors used bilstm to extract contextual semantic information and utilized dependency syntax trees to extract features between entities. this approach achieved parameter sharing between the two subtasks, effectively alleviating the issue of error accumulation and improving extraction efficiency. however, it heavily relies on features generated by external tools and does not fully integrate the two subtasks. @xcite and @xcite utilize a sequence labeling approach to design labels, where relationship categories and entity information are designed as different labels. each word is assigned an appropriate label and decoded based on these labels. however, since a word can only be assigned one label, the extraction efficiency of these models decreases as the complexity of relationships (such as entity overlap) increases. @xcite builds upon sequence labeling by treating entity information as parameters of the relationship. through reinforcement learning, it enhances the interaction between entities and relationships, addressing the extraction of overlapping relationships. @xcite uses a pointer network to label sentences, viewing the relationship extraction process as a mapping between two entities. it first extracts the subject in the sentence and then extracts the corresponding relationship and object based on the subject. this labeling approach solves the problem of weak expression capability in sequence labeling but may encounter label imbalance issues, requiring parameter tuning to address. some researchers @xcite @xcite drew inspiration from machine translation, treating relation extraction as a sequence-to-sequence generation problem. they treat triplets as sequences to be translated, using a copying mechanism to find entities from the source sentence. @xcite proposes the seq2umtree model, which addresses the label bias issue in sequence-to-sequence methods by shortening the decoding length and using tree-based decoding. treating relationship extraction as a table-filling problem @xcite ) is also a commonly used method in joint extraction. @xcite , @xcite and @xcite generate a table for each relationship, labeling the start and end characters of the corresponding entities in the relationship table, and extract triplets using joint decoding. @xcite builds upon the table-filling approach by incorporating relationship information, strengthening global connections between same-position words in different relationship tables to achieve joint extraction. ## task definition and labeling scheme given a sentence @xmath0, x 2 , . . . , x m ], where x i is the i-th word in the sentence, the objective is to extract triples @xmath1, y 2 , . . . , y n } that match predefined relation types within the sentence. each @xmath2, r, t) represents a triple, where h represents the head entity, t represents the tail entity, and r represents the relation type.in this paper, we adopt a labeling scheme similar to @xcite design 2r + 2 labels based on the specific characteristics of the dataset. "n" represents that the text span (w i , w j ) does not form an entity, while "ht" represents that the text span (w i , w j ) forms an entity. for each type of relation, two labels are generated to annotate the head entity and tail entity, "hrh" represents that the start of the head entity of the r-th relation tuple is at position w i , and the start of the tail entity is at position w j . "trt" represents that the end of the head entity of the r-th relation tuple is at position w i , and the end of the tail entity is at position w j . both labels together form the head and tail entities of that relation. this labeling scheme for table filling generates 2r + 1 tables in total, with each relation assigned two tables labeled "hrh" and "trt" respectively. a separate table is used solely to label which fields form entities, without distinguishing the type of entity. ## methodology in this section, we will provide a detailed introduction to the joint entity and relation extraction model based on bidirectional updating and long-term memory gate mechanism. the framework we propose is illustrated in the figure 2 and mainly consists of four parts: