## 1 Introduction
This limitation prevents models from effectively focusing on the most semantically relevant path combinations, particularly in cases involving complex multi-hop reasoning chains. Additionally, existing approaches tend to underutilize non-bridge entities that co-occur with only one target entity, despite their potential to provide valuable semantic connections [13]. To address these challenges, we propose CLEAR (Cross-document Link-Enhanced Attention for **R**elations), a novel approach that enhances cross-document relation extraction through relation-aware context filtering and path-sensitive attention mechanisms. First, the Relation-aware Context Filter improves upon entity-centered filtering by incorporating relation type descriptions into the context selection process. Unlike previous approaches that focus exclusively on entities [8], our filter leverages natural language descriptions of potential relation types (generated through rules or large language models) to guide the selection of relevant sentences. This approach ensures that context relevant to the target relation is preserved while reducing noise, addressing a key limitation in existing filtering methods. Second, we propose a Path Distance-Weighted Attention (PDWA) mechanism that explicitly models semantic distances between document paths. PDWA operates on the principle that document paths with similar semantic content should receive more attention when inferring relations. For any document path, we first identify its most relation-relevant sentence through an attention network that maps the path to the target relation space. For a pair of document paths, we compute weighted representations using an attention mechanism with the target relation representation as the query vector and sentence representations as key and value vectors. This approach dynamically adjusts attention weights based on path-pair distances, enhancing the model's ability to focus on semantically similar paths. By giving higher weights to path pairs with shorter semantic distances in the feature space, PDWA significantly improves the model's performance on complex multi-hop reasoning chains. Third, a Cross-document Information Integration module captures entity connections across documents. This component includes a lightweight mechanism to incorporate information from non-bridge entities when beneficial, while primarily focusing on optimizing the integration of information from target and bridge entities through our enhanced attention mechanisms [14]. Our contributions can be summarized as follows:  
* We introduce a relation-aware context filtering approach that incorporates relation type descriptions into the filtering process. This method improves context relevance and reducing noise. * We design a novel Path Distance-Weighted Attention mechanism that dynamically adjusts attention weights based on semantic distances between document paths. This approach improves F1 scores by 3.1% on average compared to standard attention mechanisms. * To investigate the effectiveness of our model, we conduct extensive experiments on the CodRED dataset. Our experimental results and in-depth analysis show that our model significantly outperforms all competitive baselines, including the LLMs. The paper is structured as follows: Section 2 reviews the related work on document-level and cross-document relation extraction. Section 3 introduces the proposed CLEAR framework, elaborating on its architectural design and key components. Section 4 describes the experimental methodology, including dataset preparation, baseline models, and evaluation metrics. Section 5 presents the results with detailed analysis. Finally, Section 6 concludes the study and discusses future research directions (code: [[https://github.com/zjcerwin/CLEVER](https://github.com/zjcerwin/CLEVER)]([https://github.com/zjcerwin/CLEVER](https://github.com/zjcerwin/CLEVER)) (accessed on 15 November 2024)).