A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling (2021)

Abstract: Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This “generate-mine-integrate” process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation’s table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at: https://github.com/neukg/GRTE .

## introduction relational triple extraction (rte) aims to extract triples from unstructured text (often sentences), and is a fundamental task in information extraction. these triples have the form of (subject, relation, object) , where both subject and object are entities and they are semantically linked by relation. rte is important for many downstream applications. nowadays, the dominant methods for rte are the joint extraction methods that extract entities and relations simultaneously in an end-to-end way. some latest joint extraction methods @xcite @xcite @xcite have shown their strong extraction abilities on diverse benchmark datasets, especially the abilities of extracting triples from complex sentences that contain overlapping or multiple triples. among these existing joint extraction methods, a kind of table filling based methods @xcite @xcite are attracting growing research attention. these methods usually maintain a table for each relation, and each item in such a table is used to indicate whether a token pair possess the corresponding relation or not. thus the key of these methods is to fill the relation tables accurately, then the triples can be extracted based on the filled tables. however, existing methods fill relation tables mainly based on local features that are extracted from either a single token pair @xcite or the filled history of some limited token pairs @xcite , but ignore following two kinds of valuable global features: the global associations of token pairs and of relations. these two kinds of global features can reveal the differences and connections among relations and among token pairs. thus they are helpful to both the precision by verifying the extracted triples from multiple perspectives, and the recall by deducing new triples. for example, given a sentence "edward thomas and john are from new york city, usa.", when looking it from a global view, we can easily find following two useful facts. first, the triple (edward thomas, live_in, new york) is helpful for extracting the triple (john, live_in, usa), and vice versa. this is because the properties of their (subject, object) pairs are highly similar: (i) the types of both subjects are same (both are per-sons); (ii) the types of both objects are same too (both are locations). thus these two entity pairs are highly possible to possess the same kind of relation. second, the mentioned two triples are helpful for deducing a new triple (new york, located_in, usa). this is because that: (i) located_in requires both its subjects and objects be locations; (ii) located_in is semantically related to live_in; (iii) live_in indicates its objects are locations. thus there is a clear inference path from these two known triples to the new triple. obviously, these global features are impossible to be contained in local features. inspired by above analyses, we propose a global feature-oriented table filling based rte model that fill relation tables mainly based on above two kinds of global associations. in our model, we first generate a table feature for each relation. then all relations' table features are integrated into a subjectrelated global feature and an object-related global feature, based on which two kinds of global associations are mined with a transformer-based method. next, these two kinds of mined global associations are used to refine the table features. these steps are performed multiple times so that the table features are refined gradually. finally, each table is filled based on its refined feature, and all triples are extracted based on the filled tables. we evaluate the proposed model on three benchmark datasets: nyt29, nyt24, and webnlg. extensive experiments show that it consistently outperforms the existing best models and achieves the state-of-the-art results on all of these datasets. ## related work early study @xcite @xcite often takes a kind of pipeline based methods for rte, which is to recognize all entities in the input text first and then to predict the relations for all entity pairs. however, these methods have two fatal shortcomings. first, they ignore the correlations between entity recognition and relation prediction. second, they tend to suffer from the error propagation issue. to overcome these shortcomings, researchers begin to explore the joint extraction methods that extract entities and relations simultaneously. according to the research lines taken, we roughly classify existing joint methods into three main kinds. tagging based methods. this kind of methods @xcite @xcite often first extract the entities by a tagging based method, then predict relations. in these models, binary tagging sequences are often used to determine the start and end positions of entities, sometimes to determine the relations between two entities too. seq2seq based methods. this kinds of methods @xcite @xcite often view a triple as a token sequence, and convert the extraction task into a generation task that generates a triple in some orders, such as first generates a relation, then generates entities, etc. table filling based methods. this kind of methods @xcite @xcite would maintain a table for each relation, and the items in this table usually denotes the start and end positions of two entities (or even the types of these entities) that possess this specific relation. accordingly, the rte task is converted into the task of filling these tables accurately and effectively. besides, researchers also explore other kinds of methods. for example, @xcite al. (2018 ## conclusions in this study, we propose a novel table filling based rte model that extracts triples based on two kinds of global features. the main contributions of our work are listed as follows. first, we make use of the global associations of relations and of token pairs. experiments show these two kinds of global features are much helpful for performance. second, our model works well on extracting triples from complex sentences containing overlapping triples or multiple triples. third, our model is evaluated on three benchmark datasets. extensive experiments show that it consistently outperforms all the compared strong baselines and achieves state-of-the-art results. besides, our model has a competitive inference speed and a moderate parameter size.