Entity Extraction using GAN
22 papers with code • 0 benchmarks • 1 datasets
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Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
This paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities.
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.
The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon.
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.