ET - MIER : Entity Type-guided Key Mention Identification and Evidence Retrieval for Document-level Relation Extraction (2025)

Abstract: Document-level relation extraction (DocRE) task aims to identify relations between entities in a document. In DocRE, an entity may appear in multiple sentences of a document in the form of mentions. In addition, relation inference requires the use of evidence sentences that can provide key clues to entity pairs. These make DocRE more challenging than sentencelevel relation extraction. Existing work does not fully distinguish the contribution of different mentions to entity representation and the importance of mentions in evidence sentences. To address these issues, we observe that entity types can provide consistent semantic constraints for entities of the same type and implicitly preclude impossible relations between entities, which may help the model better understand both intra- and inter-entity mentions. Therefore, we propose a novel model ET-MIER, which for the first time leverages **E**ntity **T**ypes to guide key **M**ention **I**dentification and **E**vidence **R**etrieval. In this way, entity types not only help learn better entity representation but also enhance evidence retrieval, both of which are crucial for DocRE. We conduct experiments on widely-adopted datasets and show that our model achieves state-of-the-art performance. Our code is available at: https://github.com/NEU-IDKE/ET-MIER

## introduction relation extraction (re) is a fundamental task in natural language processing (nlp) and knowledge graph construction, typically categorized into two levels: sentence-level and document-level @xcite . document-level relation extraction (docre) focuses on identifying relations between entities within a document. in docre, an entity can have multiple mentions, and multiple relations for an entity pair can exist and may be expressed across different evidence sentences. due to this complexity, docre better aligns with realworld scenarios and is, thus, more challenging. in docre, an important issue is how to learn accurate semantic representation of entities. given that the semantic information of an entity is mainly conveyed through its mentions (including type, context, and connections to other entities), many studies focus on enhancing entity representation based on these mentions. they usually use pooling functions (the most commonly used of which is logsumexp pooling @xcite ) to aggregate the semantic information in mentions into entity representation. however, since the pooling is essentially an aggregation operation, it may not be able to effectively distinguish the unique contributions of individual mentions, especially when dealing with scenarios involving complex interactions and multiple relations. as illustrated in figure 1 , the contributions of different mentions to entity representation may vary, where the mentions in the sentences [0] and [2] offer richer semantics for "gutmann" than the mention in the sentence @xcite . these mentions may also help extract evidence sentences and play a more important role in extracting the relations between ("gutmann", "cologne"). existing methods may overlook such subtle differences. while some studies generate relation-specific entity representation @xcite , this can lead to excessive parameters due to the diversity of relations in docre. therefore, effectively leveraging mentions to capture fine-grained entity semantic information remains a key challenge. to better capture entity semantics, it is crucial to apply fine-grained attention based on the contribution of each mention, enabling the model to focus on key mentions. as shown in figure 1 , entity types provide consistent semantic cues for similar entities and implicitly constrain possible relations (e.g., no "spouse" relation between per and loc), helping the model better understand both intra-and inter-entity mentions. based on the above observations, we propose for the first time leveraging entity types to guide key mention identification and evidence retrieval for docre. specifically, we propose three innovative strategies: (1) optimization of entity type representation. the existing method mainly uses entity type information to directly filter out impossible relations @xcite , without considering the semantic information contained within the entity types themselves and the differences between entity types. therefore, we propose a method to optimize entity type representation, which will further facilitate the identification of key mentions. (2) identification of key mentions guided by entity types. our goal is to generate type-specific entity representation with high discrimination of mentions. to achieve this, we carefully design a key mention identification task, which distinguishes the importance of mentions for entity representation by identifying the semantic relevance between mentions and entity types, thereby generating highquality entity representation. (3) enhancement of evidence retrieval incorporating entity types. we introduce evidence retrieval following previous work @xcite @xcite , as an auxiliary task to help the model filter out irrelevant sentences. different from these works that capture evidence sentences by integrating all mention features, we innovatively introduce the type-specific entity representation into this task, thereby focusing on local key mentions at the same time. this enables the task to extract evidences from both global and local perspectives. based on the above three strategies, we propose a novel entity type-guided key mention identification and evidence retrieval (et-mier) for docre. our contributions include: ## related work existing docre methods may be roughly divided into two categories: graph-based and transformerbased methods. graph-based docre constructs a heterogeneous graph by treating mentions, entities, or sentences as nodes to model their interactions, thereby enabling relation reasoning. representative methods include gain @xcite , sire @xcite , drn @xcite , among others. transformer-based docre leverages transformers @xcite to capture longrange dependencies among entities for relation prediction. atlop @xcite first introduces an adaptive threshold for multi-relation prediction. eider @xcite proposes an evidenceenhanced docre framework. other works have also proposed various frameworks to further enhance performance, including rsman @xcite , dreeam @xcite , aa @xcite , srf @xcite , ttm-re @xcite , amtl @xcite and @xcite , among others. additionally, several large-model-based docre methods have also emerged gradually @xcite . with respect to entity types in the docre task, sais @xcite employs entity type classification to filter out impossible relations but does original document: [0] the second river, or watsessing river, in the state of new jersey in the united states , … [6] finally, it joins the passaic river, on its way to newark bay. [8] the reformed dutch church of second river is named after this river . … pre-trained encoder training stage key mention identification ... ... ... ... ... ... type-specific entity embedding type-specific entity attention mention embedding mention attention relation extraction type-guided evidence retrieval ... ... ... ... ... ... tser [0] the second river, or wat sessing river, in the state evidence retrieval [6] finally , it joins the passaic river , on its way to newark bay . [0] the second river, or ... [1] from its source in west ... [2] from west orange, the ... [8] the reformed dutch ... original document type-guided evidence retrieval inference stage relation extraction final predicted relation evidence retrieval [0] the second river, or wat sessing river, in the state [8] the reformed dutch chur ch of second river is named after this river . ... ... ... . . . entity type representation . . . entity type optimization push . . . pooling pooling . . . . . . tser pooling figure 2: the overall architecture of our model et-mier. ## methodology our et-mier model in figure 2 consists of three main parts: a type-specific entity representation (tser) module, which aims to obtain entity representation through joint entity type optimization and key mention identification tasks; a type-guided evidence retrieval module, which extracts more relevant evidence sentences by introducing typespecific entity pair features when calculating the importance distribution of sentences; and a relation extraction module. ## conclusions in this paper, we propose an entity type-guided key mention identification and evidence retrieval method for docre. we first propose the idea of optimizing and leveraging entity types to guide the model to distinguish the importance of different mentions and obtain better type-specific entity representation based on these key mentions. we also incorporate the entity types to guide evidence retrieval, enabling the model to extract evidence sentences more comprehensively. experimental results show that our method outperforms the existing methods while maintaining good efficiency and generalization.