Entity-centered Cross-document Relation Extraction (2022)

Abstract: Relation Extraction (RE) is a fundamental task of information extraction, which has attracted a large amount of research attention. Previous studies focus on extracting the relations within a sentence or document, while currently researchers begin to explore cross-document RE. However, current cross-document RE methods directly utilize text snippets surrounding target entities in multiple given documents, which brings considerable noisy and non-relevant sentences. Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths.In this paper, we aim to address both of these shortages and push the state-of-the-art for cross-document RE. First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths. Second, we propose a cross-document RE model based on cross-path entity relation attention, which allow the entity relations across text paths to interact with each other. We compare our cross-document RE method with the state-of-the-art methods in the dataset CodRED. Our method outperforms them by at least 10% in F1, thus demonstrating its effectiveness.

## introduction relation extraction (re) aims to detect the semantic relations between a pair of target entities in a given text, which has long been a fundamental task in natural language processing (nlp). most of re studies are under the assumption that entity pairs are within a sentence (i.e., sentence-level re) @xcite @xcite @xcite or a document (i.e., documentlevel re) @xcite @xcite @xcite . another line considers the research of cross-text re, where entity pairs are separated into different text units, (i.e., crosssentence re or n-ary re) @xcite . the latest re research has moved to crossdocument re (codre), i.e., the target entities are located in different documents @xcite . as exemplified in fig. 1 , a codre model needs to first retrieve the relevant documents and then recognizes the key text paths in these documents for relation reasoning. in @xcite , the task is formalized based on the idea of distant supervision @xcite , i.e., the text paths in a bag can facilitate the relation reasoning and thus their model performs bag-level prediction over all the text paths. unfortunately, their method may suffer from at least two problems, which inevitably hinder the accurate relation inference. first, the inputs of their method are not tailormade for cross-document re. for instance, they extract text snippets surrounding the two target entities in the document as input of a bag, brings much noisy and non-relevant context information. moreover, they ignore important bridge entities in the text paths of the bag, leading to the loss of instructive and salience information for cross-document re. as can be seen in fig. 1 , the sentences containing bridge entities are necessary to reason the relations between target entities and missing them will seriously affect the reasoning process. second, their method does not make full use of the connections between text paths. for example, the pipeline model proposed by @xcite simply leverages the information of the text path in an isolated way, lacking deep consideration of the global connections of all text paths. in contrast, although their end-to-end model @xcite uses the context of all the text paths, the process of synthesizing the context is coarse-grained. the connections across multiple text paths are actually after input construction, we propose a novel cross-document re model that is equipped with a cross-path entity relation attention module to capture the connections of text paths within a document bag, inspired by @xcite ; @xcite . specifically, we build a relation matrix where each unit represents a relation between two entities belonging to the same bag. then the baglevel relation matrix is able to capture the dependencies between the relations by the attention mechanism @xcite , which allows one relation to focus on other more relevant relations in the text paths by modeling the discourse structure @xcite @xcite . we conduct experiments on the codred dataset @xcite . the results show that our model outperforms the baseline models by a large margin. in summary, our contributions can be summarized as follows: ## framework task definition given a target entity pair (e h , e t ) and a bag of n text paths @xmath0, where each path p i consists of two documents (d h i , d t i ) mentioning the head entity e h and the tail entity e t separately, the task aims to infer the relation r from r between the target entity pair, where r is a pre-defined relation type set. when multiple mentions of one entity (subject to entity id) appear in two documents respectively, this entity is said to be shared by two documents. note that the two documents in every path may share multiple entities ## conclusions in this paper, we devise an entity-based documentcontext filter to extract important snippets related to the target information for cross-document re. for relation prediction, we propose a model that considers the global dependencies across multiple text paths and performs a fine-grained reasoning process simultaneously. empirical results show that our method drastically improves the performance of cross-document relation extraction. our work can be a valuable reference for this research.