Iterative joint extraction of entities, relationships and coreferences from text sources

Machine understanding of textual documents has been challenging since the early computer era. Since the information extraction research field emerged it has inferred multiple natural language processing tasks, such as named entities recognition, relationships extraction and coreference resolution. Even though for the purpose of the end-to-end information extraction all of the three tasks are crucial, existing work has been focusing merely on one specific task at the time or at best on their connection in a pipeline. In this paper we introduce a novel iterative and joint information extraction system that interconnects all the three tasks together using iterative feature functions which use the advantage of the intermediate extractions. Furthermore, we introduce a special transformation of data into skip-mention sequences to enable the extraction of relations and coreferences using fast first-order graphical models. Additionally, the system uses an ontology as its knowledge source, as a list of inferred extraction rules, and as a data schema of extracted results. Experimental results show that the accuracy of extractions improves after each iteration. In particular, our model obtained a 15% error reduction on named entity recognition over individual models.