Nested named entity recognition is a subtask of information extraction that seeks to locate and classify nested named entities (i.e., hierarchically structured entities) mentioned in unstructured text (Source: Adapted from Wikipedia).
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In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign.
In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets.
Ranked #5 on Nested Mention Recognition on ACE 2004
Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.
Ranked #2 on Nested Named Entity Recognition on GENIA (using extra training data)
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.