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An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph aggregation.

Neural networks : the official journal of the International Neural Network Society
For the context-dependent Text-to-SQL task, the generation of SQL query is placed in a multi-turn interaction scenario. Each turn of Text-to-SQL must take historical interactive information and database schema into account. Accordingly, how to encode...

Improved biomedical word embeddings in the transformer era.

Journal of biomedical informatics
BACKGROUND: Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g.,...

Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation.

Neural networks : the official journal of the International Neural Network Society
Detecting clusters over attributed graphs is a fundamental task in the graph analysis field. The goal is to partition nodes into dense clusters based on both their attributes and structures. Modern graph neural networks provide facilitation to jointl...

BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.

Journal of biomedical semantics
BACKGROUND: Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-relat...

Synthetic data for annotation and extraction of family history information from clinical text.

Journal of biomedical semantics
BACKGROUND: The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated extraction of family history ...

Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM.

Computational intelligence and neuroscience
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers' attention. Convolutional Neural Network (CNN) model is...

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...

Behavioral correlates of cortical semantic representations modeled by word vectors.

PLoS computational biology
The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field o...

Deep Learning Techniques for Spanish Sign Language Interpretation.

Computational intelligence and neuroscience
Around 5% of the world population suffers from hearing impairment. One of its main barriers is communication with others since it could lead to their social exclusion and frustration. To overcome this issue, this paper presents a system to interpret ...

Development and application of the ocular immune-mediated inflammatory diseases ontology enhanced with synonyms from online patient support forum conversation.

Computers in biology and medicine
BACKGROUND: Unstructured text created by patients represents a rich, but relatively inaccessible resource for advancing patient-centred care. This study aimed to develop an ontology for ocular immune-mediated inflammatory diseases (OcIMIDo), as a too...