The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.
Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.
The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.
#2 best model for Relation Extraction on WebNLG
Timely analysis of cyber-security information necessitates automated information extraction from unstructured text.
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.