hierarchical meaning

HERMIT NLU is a hierarchical multi-task architecture for extracting semantic representations across different domains, showing superior performance over existing NLU systems. It captures intents, frames, and semantic structures using BiLSTM encoders and self-attention mechanisms.

Hierarchical Dynamic Coding in the brain encodes speech for comprehension. This code deciphers linguistic features, maintaining multiple representations simultaneously.

MS gradients show diverged motor-sensory patterns, anchored at extremes by motor and sensory cortices. They reveal hierarchical, distinct structural gradients from functional gradients.

Hierarchical memory organization improves recall in random word experiments. Strongest organization correlates with best performance.

Collagen plays a critical role in tissue regeneration and structure. Optimizing scaffolds to mimic the collagen architecture can enhance regeneration outcomes.

Auditory neurons exhibit hierarchical prediction error signals from midbrain to cortex in rats and mice. Prediction errors, linked to mismatch negativity, indicate a fundamental role in perception.

Each OPA1 isoform restores mtDNA content and cristae structure, with l-and s-form balance vital for proper network morphology. Both long and short forms are needed for full mitochondrial dynamics recovery.

Uniformity in nanomaterials is crucial for enhancing structural complexity and functionality. This framework presents hierarchical organization of nanoparticles, with uniformity guiding research and highlighting potential field issues.

Kinase domains have three sectors for catalysis, substrate specificity, and regulation. These are linked to cancer mutations and targeted by allosteric inhibitors.

We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.