A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents (2021)

Abstract: Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.

## introduction the idea of distant supervision @xcite eliminates the need for manual annotation for obtaining training data for relation extraction. previously, this idea is used mostly to create sentencelevel datasets. however, the assumption of distant supervision, that the two entities of a tuple must appear in the same sentence, is overly strict. we may not find an adequate number of evidence sentences for many relations as both entities do not appear in the same sentence. the relation extraction models built on such data can find relations only for a small number of relations and the relations of most knowledge bases (kbs) will be out of the reach of such models. to address this issue, we propose a multi-hop relation extraction task where the subject and object entities of a tuple can appear in two different documents, and these two documents are connected via some common entities. we can create a chain of entities from the subject entity to the object entity of a tuple via the common entities across multiple documents. each link in this chain represents a relation between the entities located at the endpoints of the link. we can determine the relation between the subject and object entities of a tuple by following this chain of relations. this approach can give training instances for more relations than sentence-level distant supervision. following the proposed multi-hop approach, we create a two-hop relation extraction dataset for the task. each instance of this dataset has two documents, where the first document contains the subject entity and the second document contains the object entity of a tuple. these two documents are connected via at least one common entity. this idea can be extended to create an n-hop dataset. we also propose a hierarchical entity graph convolutional network (hegcn) model for the task. our proposed model has two levels of graph convolutional networks @xcite . the first-level gcn of the hierarchy is applied to the entity mention level graph of every document to capture the relations among the entity mentions within a document. the second-level gcn of the hierarchy is applied on a unified entity-level graph, which is built using all the unique entities present in the document chain. this entity-level graph can be built on the document chain of any length and it can capture the relations among the entities across the multiple documents in the chain. our proposed hegcn model improves the performance on our two-hop dataset. to summarize, the following are the contributions of this paper: ## task formalization multi-hop relation extraction can be defined as follows. consider two entities, a subject entity e s and an object entity e o , and a chain of documents 3 related work ## dataset construction we create a two-hop relation extraction dataset from a multi-hop question-answering (qa) dataset wikihop @xcite . @xcite defined the multi-hop qa task as follows: given a set of supporting documents d s and a set of candidate answers c a which are mentioned in d s , the goal is to find the correct answer a * ∈ c a for a question by drawing on the supporting documents. they used wikipedia articles and wikidata @xcite tuples for creating this dataset. each positive tuple (e s , e o , r p ) in wikidata has two entities, a subject entity e s and an object entity e o , and a positive relation r p between the subject and object entity. the questions are created by combining the subject entity e s and the relation r p , and the object entity e o is the correct answer a * for a given question. the other candidate answers are carefully chosen from wikidata entities so that they have a similar type as the correct answer. the supporting documents are chosen in such a way that at least two documents are needed to find the correct answer. this means the subject entity e s and the object entity e o do not appear in the same document. they used a bipartite graph partition technique to create the dataset. in this bipartite graph, vertices on one side correspond to wikidata entities, and vertices on the other side correspond to wikipedia articles. an edge is created between an entity vertex and a document vertex if this document contains the entity. as we traverse the graph starting from vertex e s , it visits many document vertices and entity vertices. this constitutes the supporting document set and candidate answer set. if the candidate answer set does not contain the object entity e o which is the correct answer, this instance is discarded. they also limited the length of the traversal to three documents. @xcite only released the supporting documents, questions, and candidate answers for their dataset. they did not release the connecting entities. we convert this wikihop dataset into a two-hop relation extraction dataset. the subject entities and the candidate entities can be easily found in the documents using string matching. we use a named entity recognizer from spacy foot_1 to find the other entities in the documents and these entities can link these documents. we find that most of the wikihop question-answer instances are two-hop instances. that means for most of the instances of wikihop dataset, there is at least one document pair in the supporting document set where the first document of the pair contains the subject entity and the second document of the pair contains the correct answer, and these two documents in the pair are directly connected via some third entity. to simplify the multi-hop relation extraction task, we fix the hop count at 2. for every instance of the wikihop dataset, we can easily find the subject entity e s and the positive relation r p from the question. the correct answer a * is the object entity of a positive tuple. (e s , a * , r p ) is the positive tuple for relation extraction. for any other candidate answer e @xmath0, the entity pair (e s , e w ) is considered as a none tuple if there exists no relation among the four pairs (e s , e w ), (e w , e s ), (e w , e o ), and (e o , e w ) in wikidata. we check for the no relation condition for these four entity pairs involving e w , e s , and e o to reduce the distant supervision noise in the dataset for none tuples. we create a none candidate set c n with each e @xmath1. we first find all possible pairs of documents from the supporting document set d s such that the first document of the pair contains the subject entity e s and the second document of the pair contains either the entity a * or one of the entities from c n . we discard those pairs of documents that do not contain any common entity. the document pairs where the second document contains the entity a * are considered as a document chain for the positive tuple (e s , a * , r p ) where r @xmath2. all other document pairs where the second document contains an entity from the set c n are considered as a document chain for none tuple (e s , e w , n one) where e @xmath3. in this way, using distant supervision, we can create a dataset for two-hop relation extraction. each instance of this dataset has a chain of documents @xmath4, e o , r). the document d s contains the subject entity e s and the document d o contains the object entity e o . the two documents are connected with at least one common entity c. there exists at least one entity chain e @xmath5. the goal is to find the relation r between e s and e o from the set r ∪ {none}. we refer to this two-hop dataset as thred (two-hop relation extraction dataset) in the remaining sections of this paper. we manually checked 100 randomly selected positive samples and 100 randomly selected negative samples, and found that 76% of the selected positive samples and 82% of the selected negative samples are accurate. ## proposed hegcn model we propose a hierarchical entity graph convolutional network (hegcn) for multi-hop relation ## experiments 6.1 baselines we implement four neural baseline models for comparison with our proposed hegcn model. similar to our proposed model, we represent the tokens in the documents using pre-trained word embedding vectors and entity token indicator vectors. we use a document separator token when concatenating the vectors of two documents in a chain.