Cognitive Robotics (Jan 2022)
Joint extraction of entities and relations by entity role recognition
Abstract
Joint extracting entities and relations from unstructured text is a fundamental task in information extraction and a key step in constructing large knowledge graphs, entities and relations are constructed as relational triples of the form (subject, relation, object) or (s, r, o). Although triple extraction has been extremely successful, there are still continuing challenges due to factors such as entity overlap. Recent work has shown us the excellent performance of joint extraction models, however these methods still suffer from some problems, such as the redundancy prediction problem. Traditional methods for solving the overlap problem require triple extraction under the full class of relations defined in the dataset, however the number of relations in a sentence is much smaller than the full relational class, which leads to a large number of redundant predictions. To solve this problem, this paper decomposes the task into two steps: entity and potential relation extraction and entity-semantic role determination of triples. Specifically, we design several modules to extract the entities and relations in the sentence separately, and we use these entities and relations to construct possible candidate triples and predict the semantic roles (subject or object) of the entities under the relational constraints to obtain the correct triples. In general we propose a model for identifying the semantic roles of entities in triples under relation constraints, which can effectively solve the problem of redundant prediction, We also evaluated our model on two widely used public datasets, and our model achieved advanced performance with F1 scores of 90.8 and 92.4 on NYT and WebNLG, respectively.
