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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We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.
COREFERENCE RESOLUTION DIALOGUE STATE TRACKING EVENT EXTRACTION JOINT ENTITY AND RELATION EXTRACTION MULTI-TASK LEARNING NESTED NAMED ENTITY RECOGNITION RELATION CLASSIFICATION SEMANTIC ROLE LABELING STRUCTURED PREDICTION
Bounding boxes are generated from the feature maps, where a box is an abstract representation of an NE candidate.
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output.
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions.
Subset selection from massive data with noised information is increasingly popular for various applications.
Ranked #5 on Named Entity Recognition on SciERC (using extra training data)
ACTION RECOGNITION FINE-GRAINED IMAGE CLASSIFICATION IMAGE GENERATION MACHINE TRANSLATION MUSIC MODELING NESTED NAMED ENTITY RECOGNITION NODE CLASSIFICATION PANOPTIC SEGMENTATION PART-OF-SPEECH TAGGING POSE ESTIMATION RELATION EXTRACTION RGB SALIENT OBJECT DETECTION SEMI-SUPERVISED IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION TEMPORAL ACTION PROPOSAL GENERATION TRAFFIC PREDICTION VISUAL OBJECT TRACKING