GENIA

In this paper, we propose a multi-criteria- based active learning approach and effec- tively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by …...

AbstractText mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text …...

AbstractWe propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We …...

AbstractThe task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately developed …...

AbstractNamed Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading …...

AbstractMention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle …...

AbstractThis paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER …...

AbstractConstituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested …...

AbstractNested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework.A natural solution is to treat the task as …...