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.
Ranked #2 on Relation Extraction on NYT24
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Ranked #1 on Nested Named Entity Recognition on GENIA (using extra training data)
The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.
Ranked #8 on Relation Extraction on WebNLG
Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon.
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.
Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction.